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The Southern African Institute of Mining and Metallurgy OFFICE BEARERS AND COUNCIL FOR THE 2019/2020 SESSION

PAST PRESIDENTS

Honorary President

* W. Bettel (1894–1895) * A.F. Crosse (1895–1896) * W.R. Feldtmann (1896–1897) * C. Butters (1897–1898) * J. Loevy (1898–1899) * J.R. Williams (1899–1903) * S.H. Pearce (1903–1904) * W.A. Caldecott (1904–1905) * W. Cullen (1905–1906) * E.H. Johnson (1906–1907) * J. Yates (1907–1908) * R.G. Bevington (1908–1909) * A. McA. Johnston (1909–1910) * J. Moir (1910–1911) * C.B. Saner (1911–1912) * W.R. Dowling (1912–1913) * A. Richardson (1913–1914) * G.H. Stanley (1914–1915) * J.E. Thomas (1915–1916) * J.A. Wilkinson (1916–1917) * G. Hildick-Smith (1917–1918) * H.S. Meyer (1918–1919) * J. Gray (1919–1920) * J. Chilton (1920–1921) * F. Wartenweiler (1921–1922) * G.A. Watermeyer (1922–1923) * F.W. Watson (1923–1924) * C.J. Gray (1924–1925) * H.A. White (1925–1926) * H.R. Adam (1926–1927) * Sir Robert Kotze (1927–1928) * J.A. Woodburn (1928–1929) * H. Pirow (1929–1930) * J. Henderson (1930–1931) * A. King (1931–1932) * V. Nimmo-Dewar (1932–1933) * P.N. Lategan (1933–1934) * E.C. Ranson (1934–1935) * R.A. Flugge-De-Smidt (1935–1936) * T.K. Prentice (1936–1937) * R.S.G. Stokes (1937–1938) * P.E. Hall (1938–1939) * E.H.A. Joseph (1939–1940) * J.H. Dobson (1940–1941) * Theo Meyer (1941–1942) * John V. Muller (1942–1943) * C. Biccard Jeppe (1943–1944) * P.J. Louis Bok (1944–1945) * J.T. McIntyre (1945–1946) * M. Falcon (1946–1947) * A. Clemens (1947–1948) * F.G. Hill (1948–1949) * O.A.E. Jackson (1949–1950) * W.E. Gooday (1950–1951) * C.J. Irving (1951–1952) * D.D. Stitt (1952–1953) * M.C.G. Meyer (1953–1954) * L.A. Bushell (1954–1955) * H. Britten (1955–1956) * Wm. Bleloch (1956–1957) * H. Simon (1957–1958)

Mxolisi Mgojo President, Minerals Council South Africa Honorary Vice Presidents Gwede Mantashe Minister of Mineral Resources, South Africa Ebrahim Patel Minister of Trade and Industry, South Africa Blade Nzimande Minister of Science and Technology, South Africa President M.I. Mthenjane President Elect V.G. Duke Senior Vice President I.J. Geldenhuys Junior Vice President Z. Botha Incoming Junior Vice President W.C. Joughin Immediate Past President A.S. Macfarlane Co-opted to Office Bearers S. Ndlovu Honorary Treasurer V.G. Duke Ordinary Members on Council B. Genc W.C. Joughin G.R. Lane E. Matinde G. Njowa B. Ntsoelengoe

S.M Rupprecht N. Singh A.G. Smith M.H. Solomon A.T. van Zyl E.J. Walls

Co-opted Members T. Makgala

R.C.W. Webber-Youngman

Past Presidents Serving on Council N.A. Barcza R.D. Beck J.R. Dixon H.E. James R.T. Jones C. Musingwini S. Ndlovu

J.L. Porter S.J. Ramokgopa M.H. Rogers D.A.J. Ross-Watt G.L. Smith W.H. van Niekerk

G.R. Lane–TPC Mining Chairperson Z. Botha–TPC Metallurgy Chairperson G. Dabula–YPC Chairperson S. Manjengwa–YPC Vice Chairperson Branch Chairpersons Botswana Vacant DRC

S. Maleba

Johannesburg

D.F. Jensen

Namibia

N.M. Namate

Northern Cape

F.C. Nieuwenhuys

Pretoria

S. Uludag

Western Cape

A.B. Nesbitt

Zambia

D. Muma

Zimbabwe

C.P. Sadomba

Zululand

C.W. Mienie

*Deceased * M. Barcza (1958–1959) * R.J. Adamson (1959–1960) * W.S. Findlay (1960–1961) * D.G. Maxwell (1961–1962) * J. de V. Lambrechts (1962–1963) * J.F. Reid (1963–1964) * D.M. Jamieson (1964–1965) * H.E. Cross (1965–1966) * D. Gordon Jones (1966–1967) * P. Lambooy (1967–1968) * R.C.J. Goode (1968–1969) * J.K.E. Douglas (1969–1970) * V.C. Robinson (1970–1971) * D.D. Howat (1971–1972) * J.P. Hugo (1972–1973) * P.W.J. van Rensburg (1973–1974) * R.P. Plewman (1974–1975) * R.E. Robinson (1975–1976) * M.D.G. Salamon (1976–1977) * P.A. Von Wielligh (1977–1978) * M.G. Atmore (1978–1979) * D.A. Viljoen (1979–1980) * P.R. Jochens (1980–1981) G.Y. Nisbet (1981–1982) A.N. Brown (1982–1983) * R.P. King (1983–1984) J.D. Austin (1984–1985) H.E. James (1985–1986) H. Wagner (1986–1987) * B.C. Alberts (1987–1988) * C.E. Fivaz (1988–1989) * O.K.H. Steffen (1989–1990) * H.G. Mosenthal (1990–1991) R.D. Beck (1991–1992) * J.P. Hoffman (1992–1993) * H. Scott-Russell (1993–1994) J.A. Cruise (1994–1995) D.A.J. Ross-Watt (1995–1996) N.A. Barcza (1996–1997) * R.P. Mohring (1997–1998) J.R. Dixon (1998–1999) M.H. Rogers (1999–2000) L.A. Cramer (2000–2001) * A.A.B. Douglas (2001–2002) S.J. Ramokgopa (2002-2003) T.R. Stacey (2003–2004) F.M.G. Egerton (2004–2005) W.H. van Niekerk (2005–2006) R.P.H. Willis (2006–2007) R.G.B. Pickering (2007–2008) A.M. Garbers-Craig (2008–2009) J.C. Ngoma (2009–2010) G.V.R. Landman (2010–2011) J.N. van der Merwe (2011–2012) G.L. Smith (2012–2013) M. Dworzanowski (2013–2014) J.L. Porter (2014–2015) R.T. Jones (2015–2016) C. Musingwini (2016–2017) S. Ndlovu (2017–2018) A.S. Macfarlane (2018–2019)

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VOLUME 120 NO. 1 JANUARY 2020

Contents Journal Comment

by W.C. Joughin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv President’s Corner: 2020, a year ringing of hope

by M.I. Mthenjane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi–vii

DEEP MINING PAPERS Full-scale rockbolt testing in the laboratory: Analysis of recent results S.A. Hagen, T. Larsen, A. Berghorst, and G. Knox. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Rockbolting is a method used for rock reinforcement in underground mining and tunnelling. A rockbolt test rig has been developed for full-scale testing for pull, shear, and combination pull-shear tests. This paper describes the principles behind this quasi-static, full-scale testing and includes the results and analyses of recent tests performed on different types of rockbolts.

ISSN 2225-6253 (print) ISSN 2411-9717 (online)

A practical design approach for an improved resin-anchored tendon B.R. Crompton and J. Sheppard. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Directory of Open Access Journals

The support strength of a resin-grouted tendon is often constrained by the resin annulus between the tendon and the borehole. This paper documents a practical investigation into the effectiveness of typical resin tendon designs in large annulus installations and the development of an improved tendon design for such cases.

THE INSTITUTE, AS A BODY, IS NOT RESPONSIBLE FOR THE STATEMENTS AND OPINIONS ADVANCED IN ANY OF ITS PUBLICATIONS.

Copyright© 2020 by The Southern African Institute of Mining and Metallurgy. All rights reserved. Multiple copying of the contents of this publication or parts thereof without permission is in breach of copyright, but permission is hereby given for the copying of titles and abstracts of papers and names of authors.

Permission to copy illustrations and short extracts from the text of individual contributions is usually given upon written application to the Institute, provided that the source (and where appropriate, the copyright) is acknowledged.

Apart from any fair dealing for the purposes of review or criticism under The Copyright Act no. 98, 1978, Section 12, of the Republic of South Africa, a single copy of an article may be supplied by a library for the purposes of research or private study. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without the prior permission of the publishers. Multiple copying of the contents of the publication without permission is always illegal.

U.S. Copyright Law applicable to users In the U.S.A. The appearance of the statement of copyright at the bottom of the first page of an article appearing in this journal indicates that the copyright holder consents to the making of copies of the article for personal or internal use. This consent is given on condition that the copier pays the stated fee for each copy of a paper beyond that permitted by Section 107 or 108 of the U.S. Copyright Law. The fee is to be paid through the Copyright Clearance Center, Inc., Operations Center, P.O. Box 765, Schenectady, New York 12301, U.S.A. This consent does not extend to other kinds of copying, such as copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale.

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Artificial intelligence and big data analytics in mining geomechanics J. McGaughey. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 A process and software framework is presented that solves the prerequisite 4D data integration problem, setting the stage for routine application of AI or machine learning methods in mining geomechanics. The system rationale and structure are described with reference to specific AI challenges in rock engineering.

The need for improved layout design criteria for deep tabular stopes Y. Jooste and D.F. Malan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Aspects related to two popular design criteria used in the deep gold mines of South Africa, i.e. average pillar stress (APS) and energy release rate (ERR), are described.

A numerical modelling study is presented that illustrates the effect of total closure on the simulated APS and ERR values of remnants. It is recommended that stress measurements be conducted below remnant areas to better calibrate the numerical models.

International Advisory Board R. Dimitrakopoulos, McGill University, Canada D. Dreisinger, University of British Columbia, Canada M. Dworzanowski, Consulting Metallurgical Engineer, France E. Esterhuizen, NIOSH Research Organization, USA H. Mitri, McGill University, Canada M.J. Nicol, Murdoch University, Australia E. Topal, Curtin University, Australia D. Vogt, University of Exeter, United Kingdom

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The geotechnical evolution of deep-level mechanized destress mining at South Deep mine P.G. Andrews, R.J. Butcher, and J. Ekkerd. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 The main geotechnical challenges for successfully mining the South Deep orebody were to introduce a mechanized mining method to destress and then cost-effectively extract the massive, low-grade orebody. This paper outlines the geotechnical processes used to overcome issues including ground support, seismicity, and rock mass conditions, and highlights the key leanings from a deep-level massive mine’s evolution over time.

The role of rock mass heterogeneity and buckling mechanisms in excavation performance in foliated ground at Westwood Mine, Quebec L. Bouzeran, M. Pierce, P. Andrieux, and E. Williams. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Operations at Westwood mine in Quebec, Canada were temporarily halted in May 2015 after three large-magnitude seismic events occurred over two days. Geotechnical characterization of the rock mass was carried out and numerical back-analyses of several locations were completed. The objectives of the back-analyses were to better understand the mechanisms controlling rock mass performance and to obtain a calibrated model for predictive stoping simulations.

Applications for the Hovermap autonomous drone system in underground mining operations E. Jones, J. Sofonia, C. Canales, S. Hrabar, and F. Kendoul. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 This paper discusses the development and current state of the Hovermap autonomous drone system in underground mines and areas inaccessible to GPS instrumentation. The examples focus principally on improving safety through a better understanding of the rock mass behaviour and failure mechanisms.

Anisotropic rock mass behaviour in high-displacement ground at CSA mine G.B. Sharrock and B. Chapula. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 This paper summarizes key findings from a 39-month study at CSA mine on factors controlling anisotropic ground behaviour. The aim was to understand factors controlling high-displacement ground behaviour. The stress path induced by mining was found to significantly affect both the initiation and progression of damage in both tunnels and raises.

Addressing misconceptions regarding seismic hazard assessment in mines: b-value, Mmax, and space-time normalization J. Wesseloo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Some misconceptions with respect to seismic hazard assessment appear to be present in the industry. Where these misconceptions exist, they adversely affect the quality of seismic hazard assessment and risk management decision-making. This paper address some of these misconceptions.

Do stopes contribute to the seismic source?

L.M. Linzer, M.W. Hildyard, S.M. Spottiswoode, and J. Wesseloo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 An investigation was undertaken on the influence of the stope on seismic inversions for the scalar moment, corner frequency/source radius, and stress drop through numerical modelling using WAVE3D. The results show that the stope appears to have an appreciable effect on the seismic inversions.

Development of a remote-controlled rockbolting system for narrow-seam hard-rock mines D.M. O’Connor and T. Sertic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 This paper describes the development of a semi-automated, remotely-controlled rockbolting system for use in hard rock mines with a mining height of between 0.9 m and 1.2 m (the ULP Project). The introduction of systematic rockbolting has resulted in a decrease in rockfall-related accidents.

The Journal of the Southern African Institute of Mining and Metallurgy

VOLUME 120

JANUARY 2020

iii ◀

nal

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ment

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T

he Ninth International Conference on Deep and High Stress Mining (Deep Mining 2019) was held at the Misty Hills Conference Centre, Muldersdrift, Johannesburg from 24 to 26 June 2019. Conferences in this series have previously been hosted in Australia, South Africa, Canada, and Chile. This event, jointly organized by the South African National Institute of Rock Engineering (SANIRE), the Australian Centre for Geomechanics (ACG), and the Southern African Institute of Mining and Metallurgy (SAIMM), was attended by 196 delegates from 19 different countries. Most of the delegates were from Africa (146), but the other continents were well represented; Asia (6), Australasia (12), Europe (22), North America (5), and South America (5).

The Deep Mining series of conferences continues to contribute to our understanding of deep, high-stress mines. Around the world mines are getting deeper and the challenges of stress damage, squeezing ground, and rockbursts are ever-present and increasing.

Mining methods and support systems have evolved slowly to improve the management of excavation damage and safety of personnel, but damage still occurs and personnel are injured. Techniques for modelling and monitoring have been adapted and enhanced to help understand rock mass behaviour under high stress.

Many efficacious and dynamic support products have been developed, but our understanding of the demand and capacity of support systems remains uncertain. During the conference, 33 papers were presented addressing these topics, 11 of which have been selected for this Deep Mining edition of the SAIMM Journal.

Hagan et al. describe testing of rockbolts in full-scale laboratory conditions. This comprehensive approach includes both shear and pull testing, taking the mechanical properties of the rock mass into consideration. Crompton and Sheppard provide some practical insights into the design of resin-anchored tendons, optimizing resin mixing and the resin annulus. A new remote, mechanized bolting system for use in narrow reefs is described by O’Connor and Seritic, which could significantly improve the safety of underground workers. Limitations of South African narrow tabular deep mine layout design criteria are explained by Malan and Jooste and they discuss possible improvements by calibrating with stress measurements and rock mass monitoring. The evolution of mechanized mining and support methods for the wide reefs at South Deep gold mine is presented by Andrews, Butcher, and Ekkerd. Bouzeran et al. describe analyses of rock mass heterogeneity and buckling around excavations, which helps to understand stope drift stability in foliated ground under high stress conditions. Sharrock and Chapula provide a different perspective on similar challenges at CSA Mine, Cobar. Wesseloo presents some insights into seismic hazard and proposes a consistent terminology to avoid miscommunication. The influence of stopes on the seismic source is explored by Linzer et al. Jones et al. describe how underground stope surveys using autonomous drone systems can assist with the understanding of rock mass behaviour and failure mechanisms, which can in turn improve mine planning and design. An approach to big data analytics and artificial intelligence in rock mechanics is presented by McGaughey; this topic is becoming more important as greater quantities geotechnical and monitoring data are collected.

We are grateful to the presenters and delegates for taking time out of their busy schedules to come and share their knowledge and expertise at Deep Mining 2019. The sponsors are also thanked for their generous contributions, as are the organizing committee and technical reviewers. As always, we appreciate the dedication and organizational skills of the SAIMM secretariat.

W.C. Joughin

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VOLUME 120

JANUARY 2020

v  ◀

’s

ident

Pres

2020, a year ringing of hope

er

Corn

I

have a positive outlook on 2020, also referred to as 20Plenty. And so let me wish all SAIMM members and readers in this corner a blessed, safe, and prosperous New Year; may you find plenty of joy and happiness in your relationships and all that you do.

It’s a fortunate position that I find myself in – writing the first of the year’s President’s Corner article at the beginning of the second decade of the century, 2020.

There is a hopeful tone in the pronunciation of the year which bodes well for our country. We were delighted to see the back of 2019 as Christmas approached – it was a ‘hard’ year that demanded the most from us. My hopes for 2020 are, among others, that it will be a year of living ‘smart’, enabling us to achieve a better balance of all the plenty that lies in store. It will be no surprise, then, that technology will play a greater role in my life to enable this great ambition, both for work (inevitably) and private life, with expected outcomes of more time to myself and strengthening relationships.

I’m also excited about 2020 due to the prospect of the 8th International Platinum Conference. The conference has been held every two years since inception of the series in 2004; however, owing to unforeseen constraints, the last conference was held in 2017 in Polokwane under the theme of ‘Platinum: A Changing Industry’ and was a success. So it will be three years since the last Platinum Conference and much has happened in that time in this precious sector – surely it’s time for an update!

I was intrigued by the themes of past conferences, as listed:

u u u u u u u

2004 – Platinum, Adding Value 2006 – Platinum Surges Ahead 2008 – Platinum in Transformation 2010 – Platinum in Transition: Boom or Bust 2012 – Platinum, a Catalyst for Change 2014 – Platinum, a Metal for the Future 2017 – Platinum, a Changing Industry.

A closer scrutiny of the themes left me with an impression of an industry that is forward-looking and anticipating the future, responsive to its operating environment in both commercial and social terms, and conscientious in terms of its positive impact on society. There’s no doubt that in the past decade, since the aftermath of the Global Financial Crisis and the event of the 2012 PGM industry strike, the sustainability of the industry (in South Africa) has been in question – but it has prevailed and the fortunes of the industry have turned for the better since 2019. This has been attributable not only to a weak exchange rate against the US dollar, but also strengthening prices for the by-products gold,

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palladium, and rhodium and continued management of costs. Further, the role of the 3PGM metals (platinum, palladium, and rhodium) in improving our environment through reducing emissions from automobile exhausts has been strengthened as societies have become more assertive on the issue of climate change. Climate change risks are numerous and indiscriminate in who will be affected – we will all be affected to varying degrees.

So, platinum and its cousin elements remain important commodities in South Africa’s and the world’s socio-economic and environmental contexts. It will be nice to get an update on progress in this regard.

In relation to the Institute, we remain challenged by our financial performance, attributable largely to declining revenues from membership fees and unsatisfactory performance of our conferencing offering. We need a fundamental review of our strategy and tactical plans to arrest the decline in performance and emerge from the trough we are in. Discussions in this regard began in the latter half of 2019 and action plans are being implemented to enable us to harvest some low-hanging fruit in terms of cost reduction.

However, revenue growth is what we require for a more sustainable development of the Institute, hence the focus on responding to membership needs and expectations and developing insightful and relevant educational and technical content for our conference offering. We are also investigating the opportunity of hosting brief webinars (not longer than an hour), with relevant and focused topics for discussion and exchange of ideas.

I’m confident of our endeavours to reinforce the SAIMM as an enduring organization, continuing from the past 125 years. The Institute remains relevant to the African and global mining and minerals sectors, and we will leverage our regional and international networks and relationships for mutual success.

Wishing you the best of outcomes for 2020 and hoping to see you at our events.

M.I. Mthenjane President, SAIMM

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Full-scale rockbolt testing in the laboratory: Analysis of recent results S.A. Hagen1, T. Larsen1, A. Berghorst2, and G. Knox3

Affiliation: 1 SINTEF AS, Trondheim, Norway, Canada.

2 New Concept Mining Canada, Saskatoon, Canada.

3 New Concept Mining, South Africa.

Correspondence to: S.A. Hagen

Email:

Synopsis Rockbolting is a method used for rock reinforcement in underground mining and tunnelling. There is a large variety of different types of rockbolts with different support functions. The behaviour of a rockbolt in a rock mass depends on the function and material of the bolt itself, combined with the mechanical properties of the rock mass, deformation capacity, strength, and level of stress. Testing of rockbolts in full-scale laboratory-controlled conditions is therefore of great importance. At the rock mechanics laboratory of SINTEF and the Norwegian University of Science and Technology (NTNU) in Trondheim, a rockbolt test rig has been developed for full-scale testing for pull, shear, and combination pull-shear tests. In this paper we describe the principles behind this quasi-static full-scale testing and include the results and analyses of recent tests on different types of rockbolt. The applicability of the test rig for rockbolt selection and rock support design is also discussed.

simon.a.hagen@sintef.no

Dates:

Keywords laboratory testing, rock support, rock mechanics, rockbolting.

Received: 23 Jun. 2019 Revised: 20 Aug. 2019 Accepted: 30 Aug. 2019 Published: January 2020

How to cite:

Hagen, S.A., Larsen, T., Berghorst, A., and Knox, G. Full-scale rockbolt testing in the laboratory: Analysis of recent results.

The Southern African Insitute of Mining and Metallurgy DOI ID: http://dx.doi.org/10.17159/24119717/839/2020

This paper was first presented at the Deep Mining 2019 Conference, 24–25 June 2019 Misty Hills Conference Centre, Muldersdrift, Johannesburg, South Africa.

Introduction At the rock mechanics laboratory of SINTEF and NTNU in Trondheim (Norway), a rockbolt test rig has been developed for full-scale testing for pull, shear, and combination pull-shear tests. The test rig was developed in 1995 in conjunction with Gisle Stjern’s doctoral thesis. The test rig was financed by research funds and also by Ørsta AS, one of the leading suppliers of rockbolts. The purpose of Stjern’s work was to investigate the mechanical performance of different rockbolts under different loading conditions, with the aim of simplifying the choice of bolt type and design for a given application (Stjern, 1995). Subsequently, the bolt test rig has been used for several master’s and doctoral research projects as well as for commissioned test work. More than 35 different bolt types have been tested in the full-scale rig. The rock mechanics laboratory at SINTEF/NTNU has gained valuable experience and significant knowledge as a result of these test programmes.

An important element of this test facility is that it allows us, in a controlled and fully monitored way, to pull/shear the bolts to loads beyond their capacity. This includes testing the capacity of the fixation system using fully-grouted/resin-grouted bolts or other methods. Thus, it can also be used as a system test.

SINTEF was commissioned by New Concept Mining (NCM) to test various types of bolts in the rock mechanics laboratory. The purpose of the tests, which ran from 2016 to 2018, was to certify the bolts for use in specific mines and also certify their properties. Some of the results from these tests are presented as examples from the full-scale rockbolt test rig, and compared to standardized tests performed elsewhere on the same bolts.

Test arrangement The SINTEF/NTNU rockbolt test rig The SINTEF/NTNU rockbolt test rig consists of a rigid frame enclosing two cubic concrete blocks (see Figures 1 and 2). The two concrete blocks can be moved relative to each other in two different directions in the horizontal plane to simulate shear and tensile loads on the test bolts as shown in Figure 2. Each block measures 0.95 m along each side. To simulate hard rock conditions and secure strong fixation points, both blocks are cast from high-strength concrete (UCS approx. 120 MPa). The blocks are cured for at least 28 days after casting and before testing. The test rig has a loading capacity of 600 kN in tension and 500 kN in shear. The hydraulic loading system consists of two hollow 300 kN jacks pulling The Journal of the Southern African Institute of Mining and Metallurgy

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Full-scale rockbolt testing in the laboratory: Analysis of recent results Installing the rockbolts

Figure 1—Outline of the test rig. Extensometers are placed on the rockbolt head, tension cube, and shear cube. The load cell is placed on the bolt head. Concrete blocks (in grey) are located inside the frame (blue) of the test rig

(a) Pull test

(b) Shear test

Figure 2—Diagrams illustrating the principles of rockbolt static pull and shear tests (Li, 2010)

the tensile cube, and one ram jack pushing the shear cube. The hydraulic pressure cylinders have a maximum stroke length of 250 mm in tension and 150 mm in shear.

Roller bearings are installed between the blocks and the frame in order to guide the blocks and minimize frictional resistance. The roller bearings and frame also minimize rotation of the concrete blocks during the test. The test rig is instrumented with extensometers, load cells, and hydraulic pressure transducers. The data from these is used to generate the loaddeformation characteristics of each test. The practical accuracy of the readout is 1 kN in load and 1 mm in deformation. Strain gauge measurement can be used to obtain detailed information of the load distribution along the bolt during the test.

This test will be referred to as the SINTEF/NTNU shear or pull test in the following sections.

Test procedure

Figure 4 shows the principles for installing rockbolts in the concrete blocks. To simulate in-situ conditions, the SINTEF/ NTNU procedure requires that the bolts are tested with the same outfit as for normal installation. The two concrete blocks are placed into the frame and the alignment of the drill-holes for the specific test are checked. A hollow rubber gasket (8 mm thick, 150 mm diameter) is placed directly over the drill-hole, creating a seal when the two concrete blocks are pressed together. The seal prevents cement mortar or resin from flowing between the concrete blocks, as well as creating a gap of approximately 5 mm between the concrete blocks. This gap minimizes the influence of the joint shear resistance during a shear test. A constant load of 15–20 kN compresses the concrete blocks during the installation of the rockbolt and the curing of the cement mortar or resin.

Mixing and filling with grout is normally performed with ordinary field equipment. The drill-hole needs to be plugged at the far end and grouting is performed carefully to ensure complete filling. Curing time and water-cement ratios are important factors regarding the installation and are carefully documented. As standard for cement mortar, a curing time of a minimum of 72 hours and a water-cement ratio of 0.32 are used. Other types of bolt anchoring can also be applied, such as mechanical anchoring and friction anchoring.

Testing of bolt performance When the installation is complete, the testing will normally commence after 72 hours and the clamping force of 15–20 kN is then removed. The rockbolt head is equipped with a load cell to measure the load transferred to the head of the bolt in the test.

The nut of the bolt head is normally pretensioned to a tensile

Figure 3—Drilling holes for testing

Drilling holes for bolt installation Before the cured blocks are installed in the test rig, the rockbolt test installation boreholes are drilled to the same diameter as used in the field. Figure 3 shows percussive drilling of the test boreholes. It is important that the boreholes are correctly aligned in the concrete blocks, especially for correct installation of the rockbolts. Each pair of blocks can accommodate a maximum of 13 tests before the boreholes approach too close to the edge of the block for accurate testing. Holes that are near the edge of the block can only be used for pull tests to avoid failure of blocks.

The hole diameter can be adjusted to the specifications of the rockbolt being tested. Typical diameters are 33 mm and 48 mm.

The rockbolt length that can be accommodated is approximately 1.8–2.0 m to suit the geometry of the test rig and depending on the bolt type.

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Figure 4—Layout of test set-up when rockbolt is installed in concrete blocks, ready for testing. Sketch applies for both shear and tension tests The Journal of the Southern African Institute of Mining and Metallurgy

Full-scale rockbolt testing in the laboratory: Analysis of recent results load of 5 kN to ensure good contact. Extensometers are mounted on the bolt and the concrete blocks to measure displacement.

Before the test is conducted, the test rig hydraulic cylinders are pressurized to a 15 kN load in the test direction (shear or pull) to remove any slack in the system. Testing is then conducted at a constant deformation rate of approximately 30 mm/min until failure. The data-logging rate is 5 Hz during the whole test. After testing, the failure mode of the bolt and bolt head is carefully inspected and documented with photographs and comments.

Test results Test results include yield load, ultimate load, and deformation.

The results are reported in tabular form with graphs and photographs from the test. Shear capacity of bolts is shown as applied shear load, including the shear resistance of the joint.

Comments describe the type of failure and other factors that could be of importance for the test results. Documented bolt performance is based on a minimum of three individual test runs for both shear and pull tests. Rockbolt behaviour can be classified as stiff, ductile, and energy-absorbing from the point of view of bolt performance (Li, 2010). Figure 5 shows typical test result graphs from the SINTEF/NTNU test rig for three different bolts subjected to shear and pull tests.

Test apparatus – Direct shear and tensile test Both the direct shear test and pull test as described below are common industry testing methods. However, special tools and jigs were designed by NCM for conducting their own tests. The tests were commissioned and performed at reputable testing centres, including the CSIR in Johannesburg. The purpose of these tests was to attempt to quantify the performance of the rockbolts. The pull test results were obtained by testing an entire rockbolt grouted inside a steel tube which is cut at its mid-point.

The assembly is fitted into a tensile testing machine and pulled until the rockbolt breaks. The layout of the pull test is shown in Figure 6. The data from these tests is presented in the following section. The results from the SINTEF/NTNU testing machine will be compared to the results obtained from some of these shear and pull tests.

The standard shear test involves grouting a portion of a rockbolt in a steel tube which is cut in two places around its midpoint. This assembly is then fitted into a double shear testing jig as indicated in Figure 7.

The double shear test induces a shear failure in two positions on the test sample, and therefore in order to quantify the single shear performance of the rockbolt, the load is halved. During this test the loading head travels at approximately 30 mm/min. The test is designed so that the two outer components are supported while the middle portion moves downward. This test induces a

double shearing action on the test sample. It should be noted that during this test the loaded sample is confined within the test jig.

These tests will be referred to as the standard shear or pull test in the following sections.

Recent test results Results from recent tests undertaken at SINTEF/NTNU include data for the PAR1 and Hydrabolt manufactured by NCM. The results from the SINTEF/NTNU testing machine will be compared to the results obtained from standard pull and shear tests described above on the same bolt types.

Pull test: grout-anchored energy-absorbing rockbolt – PAR1 20 mm bolt The PAR1 bolt (Figure 8) is an energy-absorbing rockbolt designed with a paddled yielding bar. This yielding bolt is designed for use in underground mines that experience squeezing ground and/or rockbursting. The design of the PAR1 bolt is such that it can be used with a variety of encapsulated media, including cementitious grout and resin capsules. Installation was completed as per the abovementioned test procedure in 33 mm test holes drilled in the concrete blocks. The rockbolts were fully grouted with an NCM grout designed for use with rockbolts in high-temperature mines. Testing was carried out after a minimum curing time of 48 hours.

Tables I and II show pull test results for the two different methods, and load-displacement behaviours of the bolts are shown in Figure 9. For the SINTEF/NTNU pull test, the mean maximum load and displacement are 236 kN and 164 mm

Figure 6—Pull test layout for the standard tensile test

Figure 7—Double shear test layout for standard shear testing

Figure 8—PAR1 energy-absorbing rockbolt (NCM)

Figure 5—Performance of different rockbolts subjected to pull loading and shear loading, classified as stiff, ductile, and energy-absorbing (Li, 2010).

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Full-scale rockbolt testing in the laboratory: Analysis of recent results Table I

P ull test results for grouted anchored PAR1 20 mm energy-absorbing bolt (SINTEF, 2016) Test ID

Yielding load Maximum load Maximum displacement (kN) (kN) (mm)

Test 1 (SINTEF) Test 2 (SINTEF) Test 2 (SINTEF)

183 180 177

232 238 238

148 176 167

Mean

180

236

164

Table II

P ull test results for grouted PAR1 20 mm energyabsorbing bolts (CSIR, 2015). Test results displacement scaled as a function of loaded length* Test ID

Test 1 (std. pull) Test 2 (std. pull) N/A 228 Test 3 (std. pull) Mean

Yielding load Maximum load Maximum displacement (kN) (kN) (mm) N/A

223

165

156 N/A

229

158

N/A

227

160

* The standard test sample was longer than the SINTEF/NTNU sample. Therefore, the displacement for the standard test has been scaled as a function of the loaded length of the samples from the SINTEF/NTNU test and the standard test.

Shear test: grouted energy-absorbing rockbolt – PAR1 25 mm bolt This test was performed using a PAR1 25 mm bolt (see Figure 8).

The PAR1 25 mm bolts that were submitted for shear testing are manufactured from high strain-to-failure steel. These rockbolts are drawn from a single batch of steel from a standard production line with no special treatment during manufacturing. Installation was completed as per the abovementioned test procedure in 33 mm test holes drilled in the concrete blocks. The rockbolts were fully grouted with an NCM grout designed for use with rockbolts in hot mines. Testing was carried out after a minimum curing time of 48 hours.

Tables III and IV show shear test results for the two different methods; load-displacement behaviours of the bolts are shown in Figure 11. For the SINTEF/NTNU shear test, the mean maximum load and displacement are 327 kN and 59 mm respectively. For the direct shear test the mean maximum load and displacement are 271 kN and 21 mm respectively. Figure 12 shows the failed bolts.

Table III

INTEF/NTNU shear test results for grouted PAR1 25 S mm energy-absorbing bolt (SINTEF, 2016) Test ID

Yielding load Maximum load Maximum displacement (kN) (kN) (mm)

Test 1 (SINTEF) Test 2 (SINTEF) Test 3 (SINTEF)

96 89 89

325 323 333

56 60 60

Mean

91

327

59

Table IV

esults for shear tests on grouted PAR1 25 mm R energy-absorbing bolt (CSIR, 2016b). Maximum load adjusted for two points of support (original maximum load divided by 2) Test ID

Figure 9—Comparison of pull test results for PAR1 20 mm bolt, load displacement plots for standard pull test, and SINTEF/NTNU pull test*

Yielding load Maximum load Maximum displacement (kN) (kN) (mm)

Test 1 (std. shear) Test 2 (std. shear) Test 3 (std. shear) Test 4 (std. shear) Test 5 (std. shear)

N/A N/A N/A N/A N/A

272 271 269 270 273

22 21 21 21 22

Mean

N/A

271

21

Figure 10—Post-test view of grouted PAR1 20 mm rockbolt, SINTEF/NTNU pull test on the left (SINTEF, 2016) and the standard pull test on the right (CSIR, 2015)

respectively. For the standard pull test, the mean maximum load and displacement are 227 kN and 160 mm respectively. Figure 10 shows the failed bolts.

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Figure 11—Comparison of shear test results for the PAR1 25 mm bolt, load displacement plots for standard shear test and SINTEF/NTNU shear test The Journal of the Southern African Institute of Mining and Metallurgy

Full-scale rockbolt testing in the laboratory: Analysis of recent results

Figure 12—Post-test view of grouted PAR1 20 mm rockbolt, SINTEF/NTNU shear test on the left (SINTEF, 2017a) and the standard double shear test on the right (CSIR, 2016a)

Figure 13—Inflatable bolt, Hydrabolt 29 mm

Shear test: inflatable bolt – Hydrabolt 29 mm Figure 13 shows the 29 mm Hydrabolt. Hydrabolts used in these tests have an uninflated tube diameter of 29 mm. The wall thickness of the tube is 2.0 mm. This Hydrabolt is designed to be installed in a hole with a diameter of between 34 mm and 40 mm. For the SINTEF/NTNU test method 37 mm holes were used. The bolt was installed with a water inflation pressure of 300 bar. Testing was carried out within 10 minutes after inflation.

Tables V and VI show shear test results for the two different methods; load-displacement behaviours of the bolts are shown in Figures 14 and 15. For the SINTEF/NTNU shear test, the mean maximum load and displacement are 119 kN and 41 mm respectively. For the direct shear test, the mean maximum load and displacement are 60 kN and 11 mm respectively.

Figure 14—Comparison of shear test results for the Hydrabolt 29 mm, load displacement plots for standard shear test and SINTEF/NTNU shear test

Table V

SINTEF/NTNU shear test results, Hydrabolt 29 mm inflatabale bolt (SINTEF, 2017b) Test ID

Yielding load Maximum load Maximum displacement (kN) (kN) (mm)

Test 1 (SINTEF) Test 2 (SINTEF) Test 3 (SINTEF)

43 39 42

119 121 117

41 40 42

Mean

41

119

41

Figure 15—Post-test view of grouted 29 mm Hydrabolt, SINTEF/NTNU shear test on the left (SINTEF, 2017) and the standard double shear test on the right (CSIR, 2016b)

Discussion and analysis Table VI

Shear test results: Hydrabolt 29 mm inflatable bolt (CSIR, 2016b). Maximum load halved for double shear Test ID

Yielding load Maximum load Maximum displacement (kN) (kN) (mm)

Test 1 (std. shear) Test 2 (std. shear) Test 3 (std. shear) Test 4 (std. shear) Test 5 (std. shear)

N/A N/A N/A N/A N/A

65 58 62 62 55

9 8 13 13 12

Mean

N/A

60

11

The Journal of the Southern African Institute of Mining and Metallurgy

Comparison of pull test results (standard direct pull test – SINTEF/NTNU test) The similarity of the results obtained by the two test methods shows that tensile tests performed with the SINTEF/NTNU rockbolt testing rig can be approximated using a steel tube with a rockbolt installed in either resin or grout. The real benefit of the SINTEF/NTNU apparatus is its ability to better simulate a bolt hole like those in which rockbolts are installed underground.

Since the bolt hole used in the test is drilled into the concrete blocks, the roughness of the borehole is similar to that of the borehole underground. Another advantage of this test method VOLUME 120

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Full-scale rockbolt testing in the laboratory: Analysis of recent results over the standard test method is the fact that it is easier to simulate the actual transverse stiffness (ASTM, 2008) of an actual bolt hole.

While this may not have a significant impact on the test results for a fully encapsulated rockbolt, it can significantly impact the performance of a rockbolt that relies on some form of friction or mechanical anchoring. In such cases, the SINTEF/NTNU testing method is potentially a more accurate representation of what will be experienced underground.

Comparison of shear test results (standard direct shear test – SINTEF/NTNU test) The maximum loads and displacements of the bolts obtained with two shear test methods are quite different. Earlier shear tests carried out by Stjern (1995) show the same trend. The higher capacities found using the SINTEF/NTNU full-scale test rig may be explained by the crushing of the concrete host blocks and the grout, which facilitates bending of the bolt shank and results in almost pure tensile stresses in the bolt at failure (Stjern, 1995).

When interpreting the shear capacities of the bolts, the influence of the shear resistance from the joint was not taken into account.

The bolt can impart a wedge effect to the planes, forcing the surfaces apart and hence suspending the shear contribution from the joint when the bolt is drawn into the joint (Stjern, 1995).

In the standard shear tests as performed above, the shearing load applies somewhat of a guillotine effect to the bolts. The shear capacity results found from pure shear tests carried out in the guillotine jig can be regarded as minimum values (Stjern, 1995). The failure surfaces of the ruptured bolts tested in the full-scale rig are more comparable to bolt failures seen in-situ than those from the guillotine tests (Stjern, 1995), as can be seen in Figure 16. Shear failure of rockbolts underground is rarely a pure shear failure of a guillotine type. This is the value of the SINTEF/NTNU testing procedure.

If a rock engineer were to design for the shear capacity (load and displacement) based on the results of the standard shear test, a more extensive (and expensive) support system may be required compared to a potentially more cost-effective support system based on results from the SINTEF/NTNU test rig.

The SINTEF/NTNU testing procedure has other benefits. One such benefit is that the material used to install/test the rockbolt can be designed to approximate the host rock in which the bolt will be used. Another benefit is the ability to test a combination of tensile and shear loading in a single test (Chen, 2014).

Conclusion The pull test capacities resulting from the SINTEF/NTNU and standard tests are quite similar for the types of bolt tested. ➤ SINTEF/NTNU mean maximum load and displacement of 236 kN and 164 mm ➤ Standard pull test mean maximum load and displacement of 227 kN and 160 mm.

The SINTEF/NTNU test gave higher shear test capacities than the standard shear test. Both load and displacement are higher. ➤ For the PAR1 25 mm fully grouted energy-absorbing bolt the maximum load ratio was 1.2 and the displacement ratio was 2.8 between the two test methods. ➤ For the Hydrabolt 29 mm inflatable bolt the maximum load ratio was 2.0 and the displacement ratio was 3.7 between the two test methods.

The higher test results may be due to the fact that the loading is not purely shear, and a tensile contribution is present, but this varies with the type of bolt and bolt design (Li, 2010) The SINTEF/NTNU test produces a better representation of reality compared to standard direct tests, since the bolt is installed in simulated hard rock conditions The standard shear tests are more suitable for measuring the material minimum shear capacity of a bolt used underground.

Acknowledgements Our thanks to chief scientist and Professor II Eivind Grøv at SINTEF/NTNU for helpful suggestions regarding this paper.

References ASTM. 2008. Standard test methods for laboratory determination of rock anchor capacities by pull and drop tests. D7401. West Conshohocken, PA. Chen, Y. 2014. Experimental study and stress analysis of rock bolt anchorage performance. Journal of Rock Mechanics and Geotechnical Engineering, vol. 6. pp. 428–437.

CSIR. 2016a. Shear testing of five grout bar capsules. Certificate no. T24529. CSIR, Johannesburg, South Africa.

CSIR. 2016b. Shear testing of five Hydrabolt assemblies (Ø29 mm SAE1010, 1.8 wall thickness). Certificate no. T24358. CSIR, Johannesburg, South Africa.

CSIR. 2015. Test of ten bolts (20mm PAR1 Resin Bolts). Certificate no. T23352.

CSIR, Johannesburg, South Africa.

Li, C.C. 2009. Field observations of rock bolts in high stress rock masses. Rock Mechanics and Rock Engineering, vol. 43, no. 4. pp. 491–496.

Li, C.C. 2010. A new energy-absorbing bolt for rock support in high stress rock masses. International Journal of Rock Mechanics and Mining Sciences, vol. 47, no. 3. pp. 396–404.

SINTEF. 2017a. Full scale rock bolt testing: Testing of strength and deformation properties of 25 mm Par1 rock bolts. Report no. SBF2017F0007. Trondheim, Norway.

SINTEF. 2017b. Full scale rock bolt testing: Testing of strength and deformation properties of rock bolt type Hydrabolt 29 mm. Report no. 2017:00004.

Trondheim, Norway.SINTEF. 2016. Full scale rock bolt testing: Testing of strength and deformation properties of Mp1 and Par1 bolts. Report no.

Figure 16—A rebar bolt exposed on the advance face of a cut-and-fill mine stope. The bolt was subjected to shear loads and deviated from its original hole trace. The thick arrows point the direction of possible shear movements in the rock (Li, 2009)

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SBF2016F0470. Trondheim, Norway.

Stjern, G. 1995 Practical performance of rock bolts. Doctoral thesis, University of Trondheim, Norway.

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The Journal of the Southern African Institute of Mining and Metallurgy

A practical design approach for an improved resin-anchored tendon B.R. Crompton1 and J. Sheppard1

Affiliation: 1 New Concept Mining, South Africa.

Correspondence to: B.R. Crompton

Email:

brendanc@newconceptmining. com

Dates:

Received: 25 Jul. 2019 Revised: 14 Aug. 2019 Accepted: 30 Aug. 2019 Published: January 2020

Synopsis The use of resin-grouted tendons is a common ground support practice in the mining industry, and various tendon designs are available. The support strength of a resin-grouted tendon is often constrained by the resin annulus between the tendon and the borehole. Effective mixing of the resin is typically achieved by ensuring the resin annulus does not exceed a specified maximum limit. Therefore, in some cases, the diameter of the tendon is dictated by the maximum allowable resin annulus and minimum diameter borehole that can be drilled and not by the support design requirements. Tendons with mastic resin capsules are prone to ‘gloving’ by the capsule packaging, thereby debonding the tendon from the borehole, and compromising the mixing of the resin surrounding the tendon.

This paper documents a practical investigation into the effectiveness of typical resin tendon designs in large annulus installations, and the development of an improved tendon design for such cases.

Keywords resin, rockbolt, annulus, mixing, voids, design.

How to cite:

Crompton, B.R. and Sheppard, J. A practical design approach for an improved resin-anchored tendon.

The Southern African Insitute of Mining and Metallurgy DOI ID: http://dx.doi.org/10.17159/24119717/845/2020

This paper was first presented at the Deep Mining 2019 Conference, 24–25 June 2019 Misty Hills Conference Centre, Muldersdrift, Johannesburg, South Africa.

Introduction Various designs of tendons and resin compositions are available for ground support in mining operations. In order to optimize the support design for safety and value, it is important to assess the resin and tendon as a system and not as separate elements. This paper presents a practical investigation of the effectiveness of various resin-rockbolt systems and the design of a tendon to optimize the mixing of resin during installation and maximize the strength of the installed ground support system.

This research was focused on resin bolt applications in hard rock mines with airleg rock drills, where a combination of larger support hole diameter and inconsistent resin mixing adversely affects the installation quality of resin rockbolts. The occurrence of ‘gloving’ in mechanized and soft rock applications has been well documented (Campbell, Mould, and MacGregor, 2004; Craig, 2012). The findings are therefore relevant to resin bolting in general.

Resin annulus The thickness of the resin surrounding the installed tendon (see Figure 1) is a critical determinant of the support capacity of the rockbolt, as the mixing and resultant strength of the installed resin bolt is dependent on adherence to the resin annulus limits as specified by resin manufacturers (Ferreira, 2012).

Industry testing in soft rock applications has indicated that the optimal annulus range to maximize the bond strength of conventional ribbed bolts lies between 2.5 mm and 4.5 mm, as illustrated in Figure 2 (Canbulat et al., 2015; Mark et al., 2003). Laboratory testing using internally threaded pipes, which approximate hard rock applications, has confirmed that the optimal annulus for resin bolting is approximately 24 mm (Snyman, Ferreira, and O’Connor, 2011).

South African hard rock mines commonly use 34 mm diameter drill bits with pneumatic rock drills when drilling support holes. Measurement of 37 holes found that the drilled hole diameters varied from 32 mm to 37 mm (see Figure 3; Crompton, 2007). Although the average support hole diameter measured was 35 mm, 30% of the measurements exceeded 36 mm. Factors such as age and wear of the drill bit, adherence to discard criteria, condition of the rock drill, and the quality of the compressed air supply all impact on the hole diameter.

Given the recommended resin annulus range of 2 mm to 4 mm for resin bolting in both soft and hard rock applications, Table I indicates the mismatch between common diameters of rockbolts and the range of support hole diameters.

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A practical design approach for an improved resin-anchored tendon To alleviate the restrictions imposed by the resin annulus, a number of tendon designs are available with altered geometry in the anchoring portion of the tendon. This is typically achieved by the use of paddles or by increasing the diameter of the bolt.

Gloving Gloving occurs when the plastic cartridge of the resin capsule partially or completely encases a length of the tendon during installation. Gloving is typically accompanied by poor mixing of the resin surrounding the rockbolt. Gloving is a problem common to all resin and bolt manufacturers, and occurs in all rock types, and can happen whether the resin bolt is installed with hand-held or mechanized equipment (Campbell, Mould, and MacGregor, 2004). Despite continued research into improving the effectiveness of resin bolting systems, gloving continues to be a widespread occurrence in industry (Purcel et al., 2016).

Figure 4 shows an extreme example of gloving from dynamic laboratory testing of a resin rockbolt conducted by the authors when testing a 2.4 m sample of a common paddle-type tendon with a 45° cropped tip installed in a steel pipe.

The prevalence of gloving in resin bolt installations poses a risk in terms of the immediate support capacity of the installed rockbolts as well as the long-term corrosion protection of the steel tendons.

Figure 1—Definition of resin annulus

Corrosion protection

Figure 2—Effect of annulus on bond strength (Snyman, Ferreira, and O’Connor, 2011)

Figure 3—Support hole diameters with 34 mm diameter bit in hard rock (Crompton, (2007)

Corrosion of steel rockbolts can be problematic given the potential long-term exposure to corrosive conditions in some installations (Aziz et al., 2013). This is particularly noticeable in certain shafts where accelerated corrosion is evident on all steel products in use (see Figure 5).

Resin bolts can offer excellent protection from corrosion if the tendon is fully encapsulated by the cured resin. However, when the resin is compromised with voids, poor mixing, eccentric bolt

Figure 4—Gloving of common resin bolt geometry

Table I

Applicability of ribbed bolts in different diameter holes Bar Ø (mm)

Hole diameter (mm)

32 34 36 38

18

7

8

9

10

20

6

7

8

9

22

5

6

7

8

25

3.5

4.5

5.5

6.5

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Figure 5—Visible corrosion on steel support products The Journal of the Southern African Institute of Mining and Metallurgy

A practical design approach for an improved resin-anchored tendon position, or cracks, the tendon can be exposed to groundwater and other corrosive media. Contact between steel and certain rock types may also result in galvanic corrosion of the steel (Chandra and Daemen, 2009). Correctly installed resin can create a barrier between the tendon and surrounding rock, preventing this, provided that the resin completely surrounds the tendon.

This paper describes the practical approach taken to further investigate the acknowledged constraints for resin bolting applications and the development of a new resin bolt design to compensate for these.

Laboratory investigations To better understand the effect of different resin bolt designs on the quality of installed ground support, four key aspects of the performance of several tendon designs were investigated in the laboratory. Testing required the installation of a large number of resin bolt samples. In order to eliminate variability arising from inconsistencies in the installations, all samples tested were installed using an automated bolt installation machine (Figure 6).

This maintained consistency during installation by automating installation factors such as rotation speed, feed speed, mixing time, and hold time.

Findings from the laboratory testing are discussed in more detail below.

Centralization of resin bolts Centralization of a resin bolt in a support hole encourages consistent mixing of resin during installation, the even distribution of stress from the tendon into the resin annulus under tensile loading, and maximizes the corrosion protection ability of the resin by fully encapsulating the tendon. In order to assess the centralization of available bolt designs, 20 mm diameter paddled bolts were manufactured with 45° cropped tips and also with split tips (Figure 7), and installed into steel pipes internally threaded with a resin annulus of 9 mm.

Once the resin had cured sufficiently, samples were sliced at 50 mm intervals along their length so that the cross-section of the rockbolt and resin could be investigated. This helped

determine the degree of eccentricity for both tip designs. The tests found that neither the 45° cropped tip nor the split design ensured centralization of the rockbolts during installation.

Figure 8 shows the 50 mm segments cut from the distal 300 mm, the critical anchoring zone, of the resin bolts. The eccentric location of each rockbolt is sufficient to allow the bolts to contact the test pipes (and therefore the borehole walls underground) which is not desirable for corrosion protection.

Closer analysis of the test samples showed how the eccentric location of tendons within a larger diameter borehole increases the likelihood of gloving, voids, and unmixed resin (Figure 9).

The large annulus to one side of the offset tendon provides a space for the resin capsule to move into without being fully shredded and mixed by the tendon.

Testing was then conducted on round and ribbed bar designs by installing samples into transparent tubes with an internal diameter of 35 mm. The transparent tubes allowed observation of the tendon and resin during the installation and mixing process.

After installation, the samples were removed from the tubes (Figure 10) so that the installed resin bolts could be assessed for centralization, quality of mixing, gloving, and voids. Immediately noticeable were the eccentric location of each sample and a line of voids along the resin/interface on the side where the tendon contacted the tube wall. These voids resulted in an inconsistent coating of the rockbolt by the resin, with the steel being exposed in several places along the length of the sample.

Figure 8—Eccentricity of 20 mm diameter paddled bar with 45° tip

Figure 6—Automated bolt installation machine

Figure 7—45° tip and split tip configurations The Journal of the Southern African Institute of Mining and Metallurgy

Figure 9—Poor resin mixing due to eccentric bolt location

Figure 10—Eccentric location of 20 mm diameter ribbed bolt in 35 mm tube VOLUME 120

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A practical design approach for an improved resin-anchored tendon In practice it is almost impossible to obtain a concentric alignment of the tendon and the borehole during installation.

The effect is that while the tendon is rotating around its axis (shown in red in Figure 11) it is also rotating around the axis of the borehole (shown in blue in Figure 11). During installation, the tendon constantly rotates around the perimeter of the borehole and scrapes resin off this surface.

The still frame from an installation video seen in Figure 12 illustrates this phenomenon, with the ribs of the tendon visible as a line of black marks along the length of the borehole.

Sample tendons were manufactured with a 32 mm tri-lobe tip on the distal end of the tendon (Figure 13). The design is intended to optimize centralization of a resin bolt in boreholes with a diameter range of 32 mm to 38 mm. Samples were again installed into simulated boreholes with an internal diameter of 38 mm and then sliced into segments for analysis.

The tri-lobe tip design markedly improved both the centralization of the installed tendons and quality of the resin mixing.

Figure 14 provides a comparison between the tendon with a tri-lobe tip (upper row of segments) and a 45° cropped tip (lower row).

Gloving and mixing Gloving was prevalent in the tendons with the 45° tip, with unmixed resin being noted along the entire length of the tendons.

In several instances, the tendon was fully sleeved at points along the length of the anchoring zone, completely debonding the tendon from the resin in this critical region of the installed resin bolt (Figure 15).

By comparison, the tri-lobe tip design was found to pull almost all the capsule packaging to the distal end of the hole.

This is done by the packaging wrapping around the very tip of the bolt as it shreds the capsule.

Figure 16 illustrates this with a side-by-side comparison of two identical tendons, with the top tendon having a tri-lobe tip and the bottom tendon a 45° tip. The tendon with the tri-lobe tip has well mixed resin along the entire length of the resin bolt with the capsule packaging wrapped up at the distal end of the bolt.

Conversely, the bottom tendon with the 45˚ tip has poorly mixed resin along its length, unmixed catalyst visible, with gloving visible and unmixed resin at the distal end of the bolt.

Figure 14—Resin mixing of tri-lobe tip (upper row) compared to 45° tip Figure 11—Eccentric location of 20 mm diameter ribbed bolt in 35 mm tube

Figure 12—Scraping of resin from internal surface of the borehole

Figure 15—Gloving of test sample with 45° tip

Figure 16—Comparison of tri-lobe tip design (top) against standard 45˚ tip bolt

Figure 13—Tri-lobe tip design

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The Journal of the Southern African Institute of Mining and Metallurgy

A practical design approach for an improved resin-anchored tendon Voids in resin To assess the efficiency with which different tendon geometries mix resin, split paddle and flat paddle designs were tested.

As anticipated, both designs improve the mixing of the resin; however, both exhibited voiding around the paddled sections of the tendons (Figure 17). These voids result from curing resin being unable to flow and fill in around the rotating paddles faces during mixing. Although acceptable load capacity was still achieved when pull-testing these samples, the presence of voids may compromise the corrosion protection of the resin bolt.

To overcome the problem of voids arising from pure rotation of the resin during mixing, a design was then tested with the paddles on the bolt twisted on the bolt axis. During mixing the rotating paddles act like an auger (Figure 18), pumping the resin towards the distal end of the borehole and preventing the formation of voids behind the rotating paddles, as can be seen in Figure 19.

Load testing Laboratory short encapsulation pull tests At each stage of testing, short encapsulation pull tests (SEPTs)

Figure 17—Voiding around flat and split paddle designs

Figure 18—Voiding around flat and split paddle designs

Figure 19—Good resin fill with augured paddle desig

were conducted in the laboratory to quantify the change in the bond strength arising from each design iteration, thus allowing the performance of the rockbolt to be incrementally improved with each modification. All tests were conducted in 38 mm diameter boreholes with an embedment length of 250 mm, including the tip of the tendon.

As illustrated by the performance envelopes (Figure 20), the tendon design developed through the iterative development and testing appears to provide a more consistent and stiffer anchorage at 100 kN through a combination of improved resin mixing and the tri-lobe tip compared to a conventional ribbed bar with cropped tip.

External third-party short encapsulation testing was conducted on the 20 mm diameter configuration of the tendon design with four different resins installed into 38 mm boreholes.

Ten HelixBolt samples were tested with each resin and the results show that the tendon design provides similar support capacity with all four resins up to 110 kN. Performance remained consistent for three of the resins to 180 kN (Bierman, 2018).

Figure 21 is an overlay of the performance envelopes for the tendon with the four different resin types.

A higher resin bond stiffness is desirable in rockbolting as this prevents the opening of joints or fractures in the rock mass (Pariseau, 2007). Laboratory testing of 16, 18, and 20 mm diameter variants of the design showed that the stiffness of the resin-anchored rockbolt installed in a 38 mm hole is similar for all diameters when loaded up to 140 kN (Figure 22). The 16 mm bolt exhibited the lowest stiffness, as expected; however, the 18 mm bolts appeared to be slightly stiffer than the 20 mm bolts.

This discrepancy is believed to be a result of test variability rather than bolt performance

Figure 20—Laboratory SEPT results for 20 mm diameter standard resin bolt and HelixBolt in a 38 mm boreholes The Journal of the Southern African Institute of Mining and Metallurgy

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A practical design approach for an improved resin-anchored tendon

Figure 21—Laboratory SEPT results for 20 mm diameter HelixBolt with different resins in 38 mm boreholes

Figure 22—Laboratory SEPT results for different diameter HelixBolts in 38 mm holes

The authors attribute the consistent resin bond stiffness to the consistent geometry of the tri-lobe tip for all bolt diameters.

Underground short encapsulation pull tests Subsequent to laboratory testing, underground short encapsulation testing was conducted to corroborate the laboratory results. These tests comprised five 16 mm, five 18 mm, and three 20 mm diameter samples. The rockbolts were installed into holes that ranged in measured diameter from 34.4 to 35.2 mm using a pneumatic rock drill mounted on an airleg to insert the bolts and mix the resin. The underground test results were corrected to account for stretch in the unbonded length of steel under load, and the performance envelopes derived from the results are presented in Figure 23. Note that these underground tests were terminated when the test loads reached 110 kN for the 16 mm rockbolts, 140 kN for the 18 bolts, and at 150 kN for the 20 mm bolts (Janse van Vuuren, 2017).

Comparison of the laboratory and underground SEPT results (Figure 22 and Figure 23) shows that the laboratory tests yielded higher load capacity with less deflection than the underground

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tests. This is due to better control and repeatability of installation and testing parameters in the laboratory compared to testing underground.

Conclusions The aim of this research was to investigate the effect on the bond strength of different tendon designs for rockbolts anchored in larger diameter boreholes with resin capsules. Benchmark testing was conducted to confirm the behaviour during installation and resultant support capacity of conventionally used resin-anchored rockbolts in large resin annulus installations. Incremental testing and modification of different tendon geometries was conducted to document what improvements to installation reliability and quality could be achieved.

Laboratory testing found that the geometry at the tip of the rockbolt should be close to, or larger than, the diameter of the resin capsule being used with the rockbolt to effectively shred the capsule packaging and reduce the risk of gloving of the installed rockbolt.

The Journal of the Southern African Institute of Mining and Metallurgy

A practical design approach for an improved resin-anchored tendon

Figure 23—Normalized underground SEPT results for different diameter HelixBolts

Centralization of the rockbolt during the mixing of the resin capsule was found to be key to improving the quality of resin mixing. Non-centred rockbolts were more prone to gloving and poor mixing due to the eccentric rotation in the support hole.

Rockbolts with paddle-type tendons were found to result in the best quality resin mix (once centralized). However, deformations in a rockbolt’s cross-section create voids in the resin as the mixed resin is unable to flow in behind the paddles during rotation of the bar. These voids reduce the load-bearing capacity of the installed rockbolt and expose the steel to water and corrosive constituents in the host rock.

The addition of an auger, or helix, feature in the area of the rockbolt mixing the resin induces axial flow of the resin along the length of the rockbolt during rotation. This pushes the resin to the top of the hole, where anchoring is critical, and fills the voids created by the mixing geometry on the rockbolt.

The research led to a design that improved resin mixing, reduced the instance of gloving in applications with large resin annuli up to 11 mm (16 mm diameter bolt in 38 mm hole) and minimized voids in the resin annulus.

Laboratory and underground test results confirm that the derived rockbolt design can be installed in a resin bolting application with an 11 mm annulus and, with a 250 mm bond length, achieved loads in excess of 160 kN with an 16 mm diameter bolt, 170 kN with an 18 mm bolt, and 190 kN with a 20 mm bolt.

References Aziz, N., Craig, P., Nemcik, J., and Hai, F.I. 2013. Rock bolt corrosion – an experimental study. Proceedings of the Coal Operators, Conference 2013.

University of Wollongong, Australia. pp. 144–151.

Bierman, R. 2018. Results of laboratory testing and underground testing of NCM HelixBolt for Impala Platinum (March 2018). Groundwork Consulting (Pty), Johannesburg, South Africa. 12 pp.

Campbell, R., Mould, R., and MacGregor, S. 2004. Investigation into the extent and

Canbulat, I., Wilkinson, A., Prohaska, G., Mnisi, M., and Singh, N. 2005. An investigation into the support systems in South African collieries. Report no. CR231/0205/SIM302. Safety in Mines Research Advisory Committee, Johannesburg, South Africa.

Chandra, D. and Daemen, J.J. 2009. Corrosion research on rock bolts and steel sets for sub-surface reinforcement of the Yucca Mountain repository. Report no. TR-06001. University of Reno, Reno, NV. Craig, P. 2012. Addressing resin loss and gloving issues at a mine with coal roof.

Proceedings of the 12th Coal Operators’ Conference. University of Wollongong and Australasian Institute of Mining and Metallurgy. pp. 120–128 Crompton, B. 2007. Hole survey exercise. Report no. 20070207BC. New Concept Mining, Johannesburg, South Africa.

Ferreira, P. 2012. A perspective on underground support technologies in Southern African platinum mines to reduce safety risks and enhance productivity.

Proceedings of the Fifth International Platinum Conference: ‘A catalyst for change’, Sun City, South Africa, 17–21 September 2012. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 445–481.

Janse van Vuuren, J. 2017. Phase 2 evaluation on resin rock support tendon performance. Report no. NCM 1708-826. Saxum Mining, Johannesburg, South Africa. 23 pp Mark, C., Compton, C.S., Dolinar, D.R., and Oyler, D.C. 2003. Field performance testing of fully grouted roof bolts. Proceedings of the 2003 SME Annual Meeting, Cincinnati. Society for Mining, Metallurgy and Exploration, Littleton, CO. pp 1–8.

Pariseau, W.G. 2007. Design Analysis in Rock Mechanics. Taylor & Francis, London.

Purcel, J., Vandermaat, D., Callan, M., and Craig, P. 2016. Practical investigations into resin anchored roof bolting parameters. Proceedings of the 16th Coal Operators' Conference. Naj Aziz, N. and Kininmonth, B. (eds.). University of Wollongong. pp. 53–63.

Snyman, W., Ferreira, P.H., and O’Connor, D. 2011. The new generation polyester

mechanisms of gloving and un-mixed resin in fully encapsulated roof bolts.

resin capsule support for rock bolt solutions to the mining industry. Proceedings

Proceedings of Coal 2004: Coal Operators' Conference. Aziz, N. (ed.). University

of the 6th Southern African Base Metals Conference, Phalaborwa, South Africa.

of Wollongong and Australasian Institute of Mining and Metallurgy.

Southern African Institute of Mining and Metallurgy, Johannesburg.

pp. 203–214.

pp. 259–272.

The Journal of the Southern African Institute of Mining and Metallurgy

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More information under ROCKTECHNOLOGY.SANDVIK

The Journal of the Southern African Institute of Mining and Metallurgy

Artificial intelligence and big data analytics in mining geomechanics J. McGaughey1

Affiliation: 1 Mira Geoscience Ltd, Montreal, Canada.

Correspondence to: J. McGaughey

Email:

johnm@mirageoscience.com

Dates:

Received: 25 Jun. 2019 Revised: 16 Oct. 2019 Accepted: 3 Nov. 2019 Published: January 2020

How to cite:

McGaughey, J. Artificial intelligence and big data analytics in mining geomechanics.

The Southern African Insitute of Mining and Metallurgy

Synopsis Mining geomechanics presents specific challenges to application of the closely-related methods of artificial intelligence (AI), big data, predictive analytics, and machine learning. This is because successful use of these techniques in geotechnical engineering requires four-dimensional (x, y, z, t) data integration as a prerequisite, and 4D data integration is a fundamentally difficult problem.

This paper describes a process and software framework that solves the prerequisite 4D data integration problem, setting the stage for routine application of AI or machine learning methods. The work flow and software system brings together structured and unstructured data and interpretation from drill-hole data to all types of geological, geophysical, rock property, geotechnical, mine production, fixed plant, mobile equipment, and mine geometry data, to provide a data fusion capability specifically aimed at applying machine learning to rock engineering problems.

The system does this by maintaining 3D earth model and 4D mine model geometrical data structures, upon which multiple data-sets are projected, interpolated, upscaled, downscaled, or otherwise processed appropriately for each data type so that the variables of importance for each problem can be co-located in space and time, a requirement for the application of any analytics algorithm. Documents and files can be stored, managed, and linked to data and interpretation to provide relevant metadata and contextual links, providing the platform required for AI solutions. The system rationale and structure are described with reference to specific AI challenges in rock engineering.

Keywords rock engineering, geomechanics, artificial intelligence, AI.

DOI ID: http://dx.doi.org/10.17159/24119717/847/2020

This paper was first presented at the Deep Mining 2019 Conference, 24–25 June 2019 Misty Hills Conference Centre, Muldersdrift, Johannesburg, South Africa.

Introduction Most people are aware of the AI technology revolution. From self-driving cars to medical, financial, and marketing applications, we have been exposed to its predictive power. Why have these methods not yet had a significant impact on understanding or forecasting mining geomechanics outcomes? The rewards of AI should be immense as mines get deeper and forecasting of stress-related or other rock behaviour becomes a limiting factor on safety and production. The reason for lack of success is simple—there is a fundamental barrier that makes mining geomechanics different from traditional AI applications.

AI and its close relatives, predictive analytics, machine learning, and big data (all of which in practice are either broadly synonymous terms or subsets of each other), work well when you can measure many variables on a specific entity, such as a mining machine, a length of drill core, or even an industrial process, and simultaneously record a condition that you want to be able to forecast such as machine failure, the mineral and geometallurgical properties of rock, or the output of a process. AI can uncover complex, predictive relationships among measured variables and the condition to be predicted.

That is why it is already being used with success in some corners of the mining industry, such as understanding the relationship between fleet vehicle data and maintenance requirements or predicting geometallurgical parameters from core scans.

However, in mining geomechanics, its application is far from simple. The reason for this is that the condition being predicted, such as the location and timing of a geotechnical hazard (including rockfall, rockburst, or slope failure, seismic event probability forecasting, ore dilution forecasting, or drawpoint hang-up prediction), may be related to known factors (e.g. geology, rock mass properties, fault structures, mine geometry, stress, extraction, production, stope sequencing, deformation, seismicity, blasting, and support). But those factors are in many cases not easily estimated quantifiable variables at the location where the prediction is required. The condition to be forecast (e.g. the rockburst or the slope failure) exists when and where it does because of the properties of the complex, four-dimensional,

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Artificial intelligence and big data analytics in mining geomechanics spatial and temporal natural earth and engineered mine system.

Not only are many of the factors affecting the prediction separated in space and time from the location and timing of the forecast event, but many can only be partially known, because they are inferred from models (geological models, geotechnical models, numerical stress models, etc.) that are themselves created from sparse measurement or drill-hole data.

Nevertheless, in spite of these particular challenges of applying modern AI or machine learning methods to mining geomechanics, success can be and has been, achieved. The solution is to take the focus off the mechanics of AI itself and put the focus on how these problems are set up for the application of AI methods, which is where deep domain knowledge and a mining-specific, supporting computational framework are required.

How artificial intelligence works There is much confusion in popular usage of the terms used to describe what amounts to a collection of pattern recognition algorithms. In formal usage, AI is a broad term encompassing the general field of computer simulation of human intelligence.

Machine learning is a narrower term, conventionally a subset of AI that uses computer algorithms to create a predictive mathematical model based on so-called historical training data that can be used to forecast the relative probability of future occurrences of given events.

Classes of machine-learning algorithms include decision trees, random forests, support vector machines, Bayesian inference, ensemble methods, and others. Deep learning is a subset of machine-learning algorithms that uses neural networks. The term ‘predictive analytics’ is roughly synonymous with machine learning, but more often used in a business application context.

The term ‘big data’ is conventionally reserved for very large datasets, typically comprising both structured data (such as tables of numbers) and unstructured data (documents, photos). In popular use, however, and for the purposes of this paper, I consider AI, machine learning, predictive analytics, and big data to all be effectively synonymous, and will use the term AI. For rock engineering applications, the choice of AI algorithm matters much less than correctly setting up the inputs to whatever algorithm is chosen. ‘ Artificial Intelligence is colossally hyped these days, but the dirty little secret is that is still has a long, long way to go… AI systems tend to be passive vessels dredging through data in search of correlations; humans are active engines for discerning how things work… Unlike human cognition, AI systems lack a theory of the world and how it works.’

Marcus (2017).

The truth of the above quotation underlines what we can and what we cannot hope to achieve in applying these methods to mining geomechanics.

What we may achieve by applying AI in mining geomechanics: 1. Find correlations among multiple data-sets and conditions or events we would like to forecast.

2. Create useful statistical models that quantitatively combine multiple input data-sets into meaningful output forecasts of future geomechanical behaviour.

3. establish the relative importance of individual data types in understanding future behaviour.

4. Confirm or refute assumptions concerning relationships

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between data, models, and experience and generally put our assumptions of site behaviour to the test of measured facts.

However, we will not (at least any time soon) applying AI in rock engineering: establish new conceptual or physical models that describe rock engineering behaviour AI systems easily available to us today are indeed ‘passive vessels dredging through data in search of correlations.’ Yet that is of great value in itself in mining geomechanics. It provides us with a new, sophisticated capability to understand underlying patterns in very complex data and apply those patterns as a set of rules that can be used to predict future behaviour based on the patterns of past experience. AI works in any domain by measuring features of a great many examples of something and correlating those features with a condition to be predicted.

For example, one could measure features (symptoms) of many individual patients in a medical application and label those patients according to the presence or absence of a specific medical condition. AI techniques could be deployed to comb through thousands of patient records, sort out the relative importance of multiple measured features (symptoms in the example), and create a mathematical model enabling the estimation of the probability of any new patient having the specific condition. AI does this by measuring the important features and combining them according to the learned relationship between the features and the probability of having the condition. The process of uncovering the relationship between measured features and the condition of interest is called training.

By analogy, the example above can be applied to many problems in rock engineering and, by further analogy, to the medical diagnostic case, it can be of tremendous practical value to understand the likely existence of a specific condition of importance (e.g., high probability of failure) that can be addressed with practical remediation measures. That remains true whether or not the underlying root causes of the conditions to remediated are fully understood. Nevertheless, in mining geomechanical applications of AI, unlike in many other domains, we prefer to use AI algorithms that are not black boxes, but rather reveal as much as possible about relationships among data, models, and outcomes.

Challenge in applying artificial intelligence to mining geomechanics The central challenge in applying AI to mining geomechanics problems stems from a simple fact: the condition (including rock fall and slope failure ) whose location and timing that we want to forecast results from a complex interplay of factors in a fourdimensional, dynamic system that can only partially be known.

Capturing the important factors from this complex system for AI training, and subsequent application to new data for providing probabilistic forecasts of where and when conditions of interest may occur, is the key challenge. Meeting this challenge requires deep domain knowledge. It is here where mining geomechanics knowledge enters the AI work flow, and it is where the application of that knowledge to capturing the most meaningful system factors will mark the difference between success and failure.

To give some examples, consider rockbursts in underground mines or slope failures in open pit mines. Rockbursts may be correlated to a host of factors such as depth, stress, stiffness, ground deformation, extraction ratio, production rate and The Journal of the Southern African Institute of Mining and Metallurgy

Artificial intelligence and big data analytics in mining geomechanics sequencing, support, blasting, span and other mine geometry factors, rock type, rock quality, proximity to geological contacts, proximity to structures, proximity to structural intersections, and orientation of structures with respect to stress and mine geometry. Similarly, slope failures may be correlated to a host of factors such as slope angle, face angle, inter-ramp angle, face height, berm width, rock quality generally, joint characterization both generally and with respect to wall orientation, water, rock type, proximity to geological contacts, proximity to structures, proximity to structural intersections, orientation of structures with respect to pit geometry, and ground deformation. The factors in play are generally site-dependent; capturing the appropriate ones requires both general and site knowledge.

Co-location in space and time is the most important concept in properly capturing the rock engineering factors that may correlate to the conditions we want to forecast. The AI training algorithms require many examples of multiple measurements on the same thing. In the medical diagnostic analogy, that same thing is the patient, and the algorithms require many patients on whom multiple factors are measured in addition to noting whether individual patients are afflicted with the condition of interest.

In mining geomechanics, it is individual locations in space and time on the rock face that stands in for the patient of the medical analogy. At those individual locations on the rock face (along a drift, in a stope, on a pit wall), many factors can be measured, some of which (e.g. stress, deformation, seismicity) change over time. The data to be assembled for the AI training is of the form:

(x, y, z, t, observation 1, observation 2, … observation m, condition = true or false).

In AI, this collection of measurements is called a feature vector. It contains the coordinates of the place (x, y , z, t) that specifies a unique location in space and time on the mine, a series of m observations (e.g. RMR, stress) that are observed or estimated at that location, and a condition or target variable that is most commonly a simple binary true or false, indicating that the condition being investigated was present or absent at that place and time (for example a rockburst or slope failure).

In practice, there are typically many thousands of individual feature vectors and a few tens of observations per feature vector.

In fact, the number of feature vectors available to us in the rock engineering domain is virtually unlimited because we are sampling over the mine geometry and time, both of which we may discretize as finely or coarsely as we choose. The number m of observation variables per feature vector is also very much at our discretion, as it is not unusual in AI to include many secondary variables (such as mathematical derivatives to test for significance of both spatial and temporal rates of change) of the primary observed or inferred variables. This expansion of observations in the feature vector by mathematical manipulation such as taking derivatives can be carried out systematically. It is in establishment of the primary observations that the crux of the challenge lies.

Co-location demands that we establish potentially useful quantities relating to each of the primary factors (e.g. rock quality, stress) that we may think have a relationship to the condition being analysed (rockfall, slope failure) at thousands of points (x, y, z, t) in the mine. In practice, this means creating a 4D model of the mine—a 3D model at several or many time steps—that contains all the primary observations believed to possibly have a relationship with the condition of interest.

Creating that 4D mine model upon which AI algorithms can be The Journal of the Southern African Institute of Mining and Metallurgy

trained to understand the patterns and relationships among data, interpretations, and the history of occurrence of specific events is the central challenge in applying AI methods to mining geomechanics. It is also in constructing the 4D model that rock engineering problems may indeed become big data. The number of data contained in the 4D model that is input to the AI algorithm is (m x n), where m is the number of observations per feature vector, n is the number of feature vectors (which is the number of digitized points on the mine model multiplied by the number of time steps, a quantity that can easily be in the millions).

The practice and pitfalls associated with the application of AI algorithms to rock engineering problems have been described elsewhere, for example in McGaughey (2019). In the remainder of this paper I focus on the most pressing challenge in the overall work flow, which is construction of the 4D mine model from which the set of feature vectors used as input in AI are derived.

A framework for successful application of AI in rock engineering A system, Geoscience INTEGRATOR (McGaughey et al., (2017), has been created that provides simple computation of the variables required to address the application of AI to mining geomechanics problems, and provides a real, working data-structure definition to the notion of a 4D mine model. It accomplishes this by maintaining 3D earth model and 4D mine model geometrical data structures, upon which multiple data-sets are projected, interpolated, upscaled, downscaled, or otherwise processed appropriately for each data type so that the variables of importance for each problem can be co-located in space and time.

Documents and files can be stored, managed, and linked to data and models to provide relevant interpretational metadata and contextual links, providing the platform required for AI solutions.

The general system configuration is shown in Figure 1. A 4D data management system sits at the core of the system. The data management system manages all relevant data types, including geological models, mine infrastructure models, drill-hole and sample data, production and blasting data, and instrument monitoring data of all types (e.g. convergence and extensometer station time series data, prism and radar data, seismic data).

Figure 1—The Geoscience INTEGRATOR system configuration. A 4D data management system resides on a server, connected to a model server for automated computation of variables (feature vector observations described in the text) and an analytics server for applying AI rules and computing event probabilities VOLUME 120

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Artificial intelligence and big data analytics in mining geomechanics It is able to automatically ingest new data from instruments or external databases. Hazard occurrence or other relevant event conditions are input automatically or manually. Most importantly, the data management system maintains an explicit model of the mine, digitized in time and space, and provides the required mappings between input data streams, the 4D mine model, and output forecasts of rock engineering conditions or events.

The data management system is directly connected to a model server, in this implementation a run-time version of the SKUAGOCAD® modelling engine, and an analytics server which can apply AI rules to new data to deliver updated reports (typically hazard assessment reports). The model server is set up to compute required variables automatically, on user demand or on a set schedule (e.g. daily). It operates under the control of the data management system, which queues required computations, supplies the input data, triggers the model server to run one of many pre-defined scripts, and receives output as newly computed observations on its internal representation of the mine model at all relevant locations (x, y, z, t).

Examples of computations that can currently be automatically run by the model server to update properties on the mine model (feature vector observations) include: ➤ Interpolate rock quality variables in a block model based on a variety of simple interpolation and geostatistical estimation techniques ➤ Interpolate time-windowed seismic source properties ➤ Compute time-windowed seismic event density ➤ Compute maximum seismic PPV over given time windows ➤ Compute proximity to contacts and structures ➤ Compute proximity to intersections of any groups of faults, dykes, geological contacts ➤ Interpolate ground deformation

➤ Compute deviatoric stress ➤ Compute fault-slip tendency ➤ Compute extraction ratio based on mine infrastructure wireframes ➤ Compute wedge and planar joint failure parameters using kinematic bench analysis.

An example of the web browser user interface illustrating a sample list of computations set up on an automatic schedule for an actual case study is illustrated in Figure 2.

The computations illustrated in Figure 2 serve to populate the 4D mine model data structure with calculated values for each observation type. The calculations are customized per site to account for the many specific parameters that typically must be set per computation (e.g. length of time windows), as well as the frequency of update per data type.

Figure 3 also shows a screenshot from the system’s web browser interface. It is showing a view of its internal data fusion table, which is a tabular display of the values of systemcomputed observations on individual mine model points (x, y, z) for a given user-selected time t. The rows of this table correspond to individual feature vectors. The complete table is the input to the AI algorithms. The output of the AI algorithms is a probabilistic estimation of the given condition being analysed (e.g., rockfall or slope failure). The output estimation is in an additional, time-varying quantity on each mine model point (x, y, z, t), describing how the probability of manifesting the condition is varying across space and time. Figure 4 is a screenshot from the web browser interface showing a subset of rules, output from the AI algorithm, which are applied to the mine model points to determine, in the particular case study example shown, relative probability of rockburst occurrence across a mine. For the example shown in Figure 4, the rockburst

Figure 2—Geoscience INTEGRATOR web interface screenshot illustrating the automated scheduling of several computations used in the application of AI-based geohazard assessment

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The Journal of the Southern African Institute of Mining and Metallurgy

Artificial intelligence and big data analytics in mining geomechanics

Figure 3—Geoscience INTEGRATOR web interface screenshot illustrating the table of feature vectors (also known as the data fusion table) for a case study. Each row of the table corresponds to one feature vector, with only four observations (columns in the table) selected for display. The table is shown for a selected time and area of the mine. The AI algorithms act on the complete table

Figure 4—Screenshot from the Geoscience INTEGRATOR web browser interface showing a subset of rules output from an AI algorithm at a case study mine site where the objective was dynamically updating a model of rockburst hazard (in this case weekly), based on an automated update of several input data streams

probability forecast is automatically updated weekly, but the schedule can be arbitrarily set to whatever is appropriate for the mine site. It is important to note that, without such an automated system, updating these computations is extremely laborious. Our experience over the years as consultants, initially carrying out these computations manually, was that the computations were sufficiently burdensome that mines would carry out updates typically annually, and at most quarterly, essential rendering the The Journal of the Southern African Institute of Mining and Metallurgy

system a tool for mid to long-term planning rather than a tactical operational guide to current areas in the mine that warrant concern.

Figure 5 shows a final, reportable operational output from the system. Once the AI rules (illustrated in Figure 4) are applied, the relative rock-burst probability can be displayed as a property on the individual mine model points. The case study example shown is for one mine level only, with relative probabilities VOLUME 120

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Figure 5—The mine model shown (by level) as the series of digitized points with computed observation values as well as the probabilistic AI-formulated hazard assessment. The display shows rockburst hazard index, as of a certain date and time, with values exceeding a given threshold shown with larger symbols

Figure 6—A 3D visualizer client called Geoscience ANALYST connects directly to the Geoscience INTEGRATOR server, enabling 4D query of the data management system to display hazard assessment results (as shown here with warmer colours indicating greater rockburst hazard probability) or any of the underlying data, model components, linked files, documents, and images

above a set threshold shown as large symbols as well as warmer colours for emphasis. The mine-level display can be captured in a PDF report and automatically dispatched on a schedule to a defined email group, or a trigger-alert can be set up if a given threshold is exceeded. All of the underlying variables, as well as

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the final output hazard assessment result, at each mine model point can be visualized for inspection and validation. All model components, variables, and hazard assessment results can also be easily visualized in 3D using the data management system’s 3D visualizer client application (See Figure 6).

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Figure 7—Schematic representation of data flow from independent data sources (top) to the data management system (centre), with connection to the server-based 3D spatial modelling engine for updating the 4D model in response to new data (right), input-output to the analytics (AI or machine learning) system (bottom), and finally hazard assessment reporting (left)

In practice, this system can be easily set up at mine site on conventional hardware or as a cloud-hosted deployment (both have been done). Data sizes are manageable with large, but not extraordinary, demands required on storage capacity. Whether deployed on site or cloud-hosted, Geoscience INTEGRATOR can be connected to multiple data sources at the mine site in several ways. Users can manually update slowly changing data such as mine infrastructure geometry or block models through a manual drag-and-drop into specified folders on the file network system for automated import. These monitoring folders can also be used for machine-to-machine communication, typically as csv files automatically output from monitoring systems (such as microseismic or ground deformation). The system can also be customized to pull directly from third-party databases (such as production databases). Because all data relevant to the hazard assessment is contained within this single data warehouse, it provides a single point from which to query and access any relevant data. In fact, some mine sites use the system for this data warehouse purpose alone. Figure 7 provides a schematic representation of the data flow.

Conclusion AI can be successfully applied to complex mining geomechanics problems. Doing so requires focusing on the primary challenge of setting up the problem rather than on the AI algorithms themselves, most of which will provide value if the problem is properly set up. Developing the proper inputs for AI in rock engineering requires mapping the complex, 4D mine and earth model system to a proper data structure in which the many multiThe Journal of the Southern African Institute of Mining and Metallurgy

disciplinary factors in play can be co-located in space and time.

Doing so in a practical, operational sense requires implementation of a 4D data management system coupled with a powerful spatial modelling engine (the model server) and the AI algorithms (the analytics server). Inputs and outputs must be automated to support systematic update at a frequency that is operationally useful for tactical decision-making by operators.

Acknowledgements The author is grateful for the support of the Ultra Deep Mining Network (UDMN), administered by the Centre for Excellence in Mining Innovation (CEMI), in Sudbury, Canada, for early financial support in this research prior to its commercialization; to Glencore for providing the case study R&D site, to JSC Apatit which provided permission to show some of the figures in this paper, and to many Mira Geoscience colleagues who have contributed to the work.

REFERENCES Marcus, G. 2017. Artificial intelligence is stuck. Here’s how to move it forward. New York Times, July 29, 2017.

McGaughey, W.J., Laflèche, V., Howlett, C., Sydor, J.L., Campos, D. Purchase, J., and Huynh, S. 2017. Automated, real-time geohazard assessment in deep underground mines. Wesseloo, J. (ed.). Proceedings of the Eighth International Conference on Deep and High Stress Mining, Australian Centre for Geomechanics, Perth. pp. 521–528.

McGaughey, W.J. 2019. Data-driven geotechnical hazard assessment: practice and pitfalls. Wesseloo, J. (ed.). Proceedings of the First International Conference on Mining Geomechanical Risk, Australian Centre for Geomechanics, Perth, pp. 219–232. u VOLUME 120

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The need for improved layout design criteria for deep tabular stopes Y. Jooste1 and D.F. Malan2

Affiliation: 1 Harmony Gold, South Africa.

2 Department of Mining Engineering, University of Pretoria, South Africa.

Correspondence to: D.F. Malan

Email:

francois.malan@up.ac.za

Dates:

Received: 27 Jun. 2019 Revised: 25 Sep. 2019 Accepted: 22 Oct. 2019 Published: January 2020

Synopsis This paper was compiled to highlight the need for additional research in the area of layout design criteria for deep gold mines. It describes aspects related to two popular design criteria used in the deep gold mines of South Africa, namely average pillar stress (APS) and energy release rate (ERR). The early layout designs in the gold mines were based on trial and error and most of the recommendations of the 1924 Witwatersrand Rock Burst Committee are still valid in modern times. The introduction of APS and ERR assisted greatly to reduce areas of high stress concentrations. Both criteria are of limited use, however, as it is not clear what the maximum value of APS for remnants should be and ERR has a significant drawback as no dissipative mechanisms are incorporated to allow for non-violent failure of the rock mass. A numerical modelling study is described that illustrates the effect of total closure on the simulated APS and ERR values of remnants. It is recommended that stress measurements be conducted below remnant areas to better calibrate the numerical models. The need for additional rock mass monitoring to supplement the design criteria is also discussed.

Keywords stope layout, pillar stress, energy release rate, numerical model, remnants.

How to cite:

Jooste, Y and Malan, D.F. The need for improved layout design criteria for deep tabular stopes.

The Southern African Insitute of Mining and Metallurgy DOI ID: http://dx.doi.org/10.17159/24119717/849/2020

This paper was first presented at the Deep Mining 2019 Conference, 24–25 June 2019 Misty Hills Conference Centre, Muldersdrift, Johannesburg, South Africa.

Introduction The deep gold mines of South Africa are world-famous and more than 50 000 t of gold have been extracted from these mines since 1886 (Malan, 2017). Owing to the great depths of the workings, these mines are seismically active and rockbursts and falls of ground were the cause of many fatalities during the early years of mining. The fatality rate per 1000 employees per annum caused by falls of ground for the years from 1911 to 1943 varied from 0.72 to 1.2 (Jeppe, 1946). Compared to modern standards and the guidelines provided by societal risk curves (Joughin, 2011), these historical accident rates were not acceptable. The safety record of the mines has improved drastically in recent years and the additional improvement since 2007 is shown in Figure 1. Unfortunately, a number of recent rockburst accidents at various mines in 2017 and 2018 have resulted in an unacceptable loss of life. The increase in fatality rate in 2017 can be seen in Figure 1. These accidents have emphasized the need for additional research into methods to mitigate the risks of rockbursts. Production from the gold mines has now also dropped to multi-decade lows (Figure 2). Gold production in 2017 was only 137 t, whereas peak production was 1 000 t in 1970. Neingo and Tholana (2016) discussed mining depth and seismicity as a contributor to the recent decrease in production.

Surprisingly, even after a sustained production of 132 years, the Witwatersrand Basin is still the world’s largest resource of gold (Minerals Council South Africa, 2018). Production can therefore continue for many years if the current poor profitability of the mines can be improved and a further improvement in safety can be achieved. Mechanization is currently being investigated by the South African Mining, Extraction, Research, Development and Innovation (SAMERDI) research programme as one possible solution. The gold industry still employs more than 100 000 workers (Minerals Council South Africa, 2018) and the social impact will be great if most of these workers lose their jobs owing to large-scale mine closures in the near future. To achieve sustainability in this sector, the development of updated design criteria for these very deep tabular mine layouts is urgently required. Remnants and pillars are mined in many of the older operations (Figure 3). Although most of these extractions are carried out without incident, there is a great need to improve the criteria used to select which remnants can be mined safely. As shown in this paper, the design criteria currently used are based on elastic

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Figure 1—Fatality frequency rate in the South African gold mines from 2007 to 2017 (after Minerals Council South Africa, 2018)

rock mass behaviour. A notable exception is Harmony Gold, which currently funds the Harmony Gold Research Chair in Rock Mechanics at the University of Pretoria. A specific aspect addressed by this research is the effect of mining rate and work was initiated to investigate the applicability of elastic design criteria when mining remnant areas.

The objective of this paper is to illustrate the shortcomings of the existing design criteria and to illustrate research areas that may lead to improvements in the layouts and better hazard identification. Van der Merwe (2006) used the Coalbrook disaster as an example of lessons not learnt from disasters and deplored the dismal state of rock engineering research in South Africa.

He stated: ‘Is it conceivable that the most important lesson from Coalbrook, namely that in order to be effective at all, knowledge has to be generated before it is needed, was not learnt?’ Thirteen years after this Coalbrook paper was written, rock engineers still grapple with which are the most appropriate design criteria to use when extracting remnants, but almost no research on this topic was done during this period. This paper highlights the shortcomings of the current design criteria.

Overview of the evolution of rock engineering design criteria in the gold mining industry

Figure 2—South African gold production from 2007 to 2017 (after Minerals Council South Africa, 2018)

theory and the failure of the rock mass is not taken into account.

Total closure in some of the older mines also appears to destress some of the remnants, but there is no current methodology to objectively quantify this effect on a mine-wide scale. These aspects require urgent research, but surprisingly, there seems to be little appetite from some mining companies and the controlling bodies to fund fundamental research on design criteria and

Durrheim (2010) gave a comprehensive overview of the risk of rockbursts in deep hard-rock mines. The first damage on surface was reported as early as 1908 and the Ophirton Earth Tremors Committee was appointed to investigate the stressrelated problems in the mines. They concluded that ‘the pillars are severely strained; that they partly give way suddenly …’.

The committee recommended that the pillars be replaced by waste packs. This study was followed by the appointment of the 1915 Witwatersrand Earth Tremors Committee, which concluded that ‘The removal of reef or reefs over large areas throws the weight of superincumbent mass on the unexcavated portions or pillars which, when the depth reaches 1,000 feet or more, are unable to bear the weight and are crushed. When crushing takes place suddenly a rock burst occurs and causes a tremor.’

The committee recommended the removal of the pillars if they cannot carry the load or the use of sand filling in adjacent areas if they cannot be removed. The 1924 Witwatersrand Rock Burst

Figure 3—A remnant (the square block above the fault) that was successfully extracted at Doornfontein mine in 1962. The face on the east was stopped and the face on the west was mined using hydraulic props and caving in the back area (after Hinds, 1963)

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The need for improved layout design criteria for deep tabular stopes Committee made several practical suggestions regarding layouts (Jeppe, 1946). The following are direct quotes of the suggestions from this source (the exact meaning is not clear in all cases). ➤ The ore should be extracted as completely as possible in the first working; Pillars or remnants of reef should not be left where that is avoidable. ➤ Efforts should be made to avoid the formation of isolated areas of reef, and to that end stoping should be kept well advanced on the mine boundaries. ➤ Stoping should be concentrated at the bottom of the mine so far as practicable. ➤ When two reefs are being mined one should be worked out as completely as possible in advance of the other. ➤ Large shaft pillars should be left for vertical shafts. ➤ Main levels should be a considerable distance in the footwall, and should be stoped over.

In terms of remnants it was suggested that: ➤ The working face should be advanced rapidly and continuously. ➤ Ample support of a character that will stand shocks should be kept close up to the working face. ➤ The minimum number of persons necessary should be employed near the working face. ➤ The direction of the working face should be carefully selected with a view to safety. ➤ The hanging wall portion of the face should be kept advanced. ➤ Stope pillars should not be cut.

Rock engineers will recognize that most of these recommendations from 1924 are still being used in industry.

Close scrutiny of these empirical rules indicated that they worked in 1924 because adhering to them reduced excessive stress

concentrations. None of the earlier workers described the problem in terms of quantitative values of stress, however, as no practical techniques existed to estimate stress levels in the irregular mining layouts.

To avoid the formation of remnants, the longwall stoping method was recommended by the 1924 rockburst committee.

It was first introduced on Crown Mines in the 1930s where the longwall faces were carried in an overhand configuration. This consisted of a longwall covering three levels and the number of remnants was reduced to one instead of three (Jeppe, 1946).

This was later changed to the ‘longwall peak stoping’ method where the faces were carried in underhand configuration, but this method was discontinued in 1944. In 1942 two underhand longwall stopes were successfully introduced in the Hercules section of ERPM by Hill as part of an experiment to reduce rockbursts (Hill, 1944). This layout is shown in Figure 4. It was shown that this resulted in ‘A large reduction in the incidence and severity of pressure bursts.’ Apparently the number of rockbursts decreased from 35 in 1941 to 14 in 1943. Longwalls were unfortunately problematic in terms of grade control and it was difficult to use this layout in areas where a large number of faults and dykes were present.

Although these empirical techniques resulted in a reduction in the number and severity of rockbursts, Cook et al. (1966) wrote that by 1950: ‘It become painfully clear that that progress was reaching a standstill’. A more fundamental approach was required. To further mitigate the rockburst problem, the CSIR was approached and in 1953 the Chamber of Mines undertook to sponsor research in rock mechanics. A committee composed of Chamber staff, the mining groups, and the CSIR then guided research over a number of years. Assistance was also obtained from the Bernard Price Institute of Geophysical Research and the University of the Witwatersrand. The Research Advisor of

Figure 4—The Hercules longwall section at ERPM in December 1943 (after Hill, 1944) The Journal of the Southern African Institute of Mining and Metallurgy

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The need for improved layout design criteria for deep tabular stopes the Chamber of Mines was appointed in 1962 and the Chamber of Mines Research Laboratory was established. Cook et al.

(1966) summarized the progress that was made as a result of these efforts. A key breakthrough was that the far field rock mass behaviour could be approximated by elastic behaviour (Ryder and Officer, 1964). Cook et al. (1966) summarized the progress as follows: ‘The paper produces evidence to show that the unfractured rock strata behave elastically, while seismic observations have confirmed the close relationship that exist between active mining operations and releases of energy.

Indeed, the problem is shown by the authors to revolve around the concepts of energy release and energy dissipation and they suggest that further progress in lessening the incidence and severity of bursts can best come about through either reducing the release of energy or increasing the dissipation of energy in non-violent form’. This work was indeed a major breakthrough and in the decades that followed, strategies such as the introduction of stabilizing pillars, backfill, numerical modelling of layouts to reduce the ERR, and bracket pillars to clamp geological structures were based on the foundation laid by these researchers.

In the gold mines, the current rockburst mitigation strategies can be broadly classified into two categories. The first set of measures attempt to reduce the number of damaging seismic events occurring, especially during times when there are workers in the stopes. These include layouts to minimize stress concentrations, bracket pillars to prevent slip on geological structures, centralized blasting systems, and preconditioning.

Secondly, measures are implemented to protect the workers during rockbursts and this involves the installation of rockburstresistant support with energy absorbing capabilities. Malan and Napier (2018) recently explored this second aspect in more detail and highlighted some of the shortcomings of the approach currently used in the deep gold mines.

A key aspect of the first set of measures is to minimize ‘excessive’ stress concentrations. This is currently achieved by numerical modelling of the layouts and applying the two key design criteria, namely average pillar stress (APS) and energy release rate (ERR). It is significant that these two criteria are imperfect as they do not take into account the extensive stressrelated fracturing of the rock mass that is so ubiquitous in the deep mines. Other forms of inelastic behaviour, such as total closure, cannot adequately be taken into account by these two criteria. As many of the older mines exploit remnants, the question should be asked; to what extent these criteria are valid in remnant areas, and if they are not, what alternative method should be used to estimate the stress distribution and associated hazard. This paper investigates the drawbacks of the APS and ERR criteria in areas where remnants are being mined. Modelling of a simplified remnant geometry is included to illustrate the value of stress measurements to better identify areas of high stress concentration. The third key design criterion, excess shear stress (ESS) on geological structures, is not considered in this paper.

Average pillar stress A difficult problem faced by rock engineers is to determine if remnants can be mined safely and to estimate the stress acting on these remnants. The computation of average pillar stress (APS) is important when attempting to establish criteria for pillar design and in the analysis of the stability of tabular pillar layouts (Ryder and Jager, 2002). Pillars with a width to height

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ratio exceeding 10:1 are often assumed to have ‘infinite strength’, although this ignores possible foundation failure (Ryder and Jager, 2002). Foundation failure, which involves rupture along planes in previously intact rock, needs to be considered for highly-stressed remnants and large pillars. In the deep gold mines, foundation failure of the regional pillars is of particular concern as these can generate seismic events in excess of magnitude 3. Some of the early work on this mode of failure was conducted by Wagner and Schumann (1971). They performed laboratory tests in which the bearing strength of rock loaded with a circular stamp was investigated. For larger stamp diameters, the bearing strength of the rock was approximately four times the uniaxial compressive strength. To avoid this type of failure, an empirical rule commonly used is (Ryder and Jager, 2002): [1] where σc is the uniaxial compressive strength (UCS) of the foundation strata. This is the weakest of the hangingwall or footwall strata and fa is an empirical factor typically taken as 2.5. This empirical factor is the controversial part of this criterion as Wagner and Schumann (1971) estimated a value as high as 4 from the laboratory testing. The value of 2.5 was suggested in the 1988 rockfalls and rockburst guide (Chamber of Mine Research Organisation, 1988). As stated in this guide: ‘Laboratory tests suggest that foundation failure occurs if the average pillar stress (APS) exceeds the uniaxial strength of the host rock by approximately 3.5 times…Thus in practice, due to the presence of rock mass weaknesses and uncertainties regarding virgin horizontal stress levels, a factor of no more than 2.5 should be used.’ This is unsatisfactory, as no scientific reason is given for the value of 2.5. Jager and Ryder (1999) refers to it as ‘An approximate and conservative criterion …’. In contrast, Ryder and Jager (2002) reported work by Handley et al. (1997) which indicated that back area pillar seismicity may start at values as low as fa = 1.2. This was confirmed by FLAC modelling (York, 1998) with a strain softening model which indicated that the APS should be limited to 1.25 times the UCS. Based on these studies, it is clear that further modelling work and field experiments need to be conducted.

To compound the problem, a numerical modelling quirk may result in erroneous APS calculations. Although the APS can be readily estimated using tributary area theory for regular layouts, the evaluation of Equation [1] in practice requires that the pillar stress be computed for irregular layouts. One of the popular numerical methods used to determine pillar stresses is the displacement discontinuity method. In coarse element simulations, the results can depend on the chosen element mesh size. This was investigated by Napier and Malan (2011). For example, for a simple two-dimensional model, it was shown that more accurate pillar stress estimation can be obtained by calculating two APS values by using different grid sizes and then calculating the extrapolated APS value as the grid size tends to zero (Figure 5). Note that the APS values and trend line in the graph are only applicable to the specific geometry investigated by Napier and Malan (2011). Further studies of the effect of element size for actual three-dimensional pillar geometries are required.

A further complication regarding APS calculations is also frequently encountered in the deep gold mines. Owing to the tabular nature of the orebody and the small mining height, total closure (contact between hangingwall and footwall) can occur in some areas (Figures 6 and 7). The resulting regeneration of The Journal of the Southern African Institute of Mining and Metallurgy

The need for improved layout design criteria for deep tabular stopes of this ‘reduced modulus’ is extremely difficult. Numerical modelling of the remnant shown in Figure 6 indicated an APS value of 697 MPa when using a value of E = 70 GPa. Such a high APS did not exist in practice as the remnant was extracted safely and no large seismic events were recorded during extraction.

A more practical method to determine pillar APS would be to use overcoring stress measurement techniques and these are occasionally used in the industry. The high cost and difficulty in obtaining intact cores in highly stressed pillar areas unfortunately prevent the large-scale use of this technique.

New developments in terms of energy release rate

Figure 5—An example of the effect of element size used in the displacement discontinuity method on simulated APS. Note that this is for a single two-dimensional pillar as a function of the element grid size and the actual APS values and trend line will be different for different geometries. (Panel span, S = 120 m; central pillar width, W = 24 m) (after Napier and Malan, 2011)

stress in these old areas results in a reduction of stress on the remaining remnants. There is no reliable method to determine the extent of total closure on the reef horizon. Physical access to the old mining areas is frequently not possible and numerical modelling can only be used if it accurately reflects the inelastic rock failure and associated increase in deformation around the excavations. The modelling of off-reef failure on a mine-wide scale cannot easily be done with the existing tools. An interim solution proposed by Gurtunca and Adams (1991) was to use elastic modelling with a reduced Young’s modulus (E). This is an unsatisfactory solution and one of the problems is that calibration

A common criterion that is used in the design of the deep-level tabular mine layouts is the energy release rate (ERR) (Cook, 1963; Heunis, 1980). In the definition of this criterion, the energy release increment, ΔWA, represents the difference between the incremental work done by gravity forces acting on the rock

Figure 7—Photographs illustrating the typical mining conditions in the remnants shown above. The photographed on the left illustrates the holing into the old mining area. Total closure occurred, as seen by the compressed pack (brown patch in the face). The photograph on the right illustrates the low rate of closure in the remnant in spite of a very high simulated APS value

Figure 6—Example of a mining area with a high extraction ratio. The spacing of the gridlines is 200 m. The entire area is mined out expect for the few areas highlighted in red. The blue remnant shown in the circle was safely extracted, although elastic modelling erroneously indicated a very high APS value of 697 MPa for E = 70 GPa The Journal of the Southern African Institute of Mining and Metallurgy

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The need for improved layout design criteria for deep tabular stopes mass, ΔW, and the incremental change in the strain energy, ΔU, that is stored in or released from the rock mass when mining an increment (Napier and Malan, 2014). This is given by: [2] For tabular excavations, the mining increment is typically expressed in terms of the area mined with respect to the plan view of the stope. If the incremental area mined is designated by ΔA, then the energy release rate is given as

subjected to total closure. When the closure becomes equal to the stoping width (1000 mm in this case), stress is regenerated in the area where total closure occurred. The displacement discontinuity elements used were 2 m square elements.

Figure 9 illustrates the area of total closure for the specific case of E = 10 GPa. This results in the regeneration of stress of more than 12 MPa in the centre portion of the mined stope (Figure 10). Figure 11 illustrates the decrease in APS on the remnant for an increase in the area of total closure. Note that the

[3] For practical ERR calculations, stress analysis programs based on the displacement discontinuity boundary element method are typically used (Plewman, Deist, and Ortlepp, 1969; Deist, Georgiadis, and Moris, 1972; Ryder and Napier, 1985).

The excavation is approximated as a narrow slit and in many applications, the reef material is assumed to be rigid and to have infinite strength. The use of ERR as a criterion for layout design has been extensively discussed. It has a number of practical shortcomings as a measure of the rockburst hazard (Salamon, 1984; Napier, 1991). The most significant drawback of the ERR criterion when used with the elastic models is that no dissipative mechanisms are incorporated to allow for local on-reef failure.

Equation [3] represents the local value of the energy release at each point of the tabular excavation boundary and can be used as a measure of the local stress concentration at the stope face.

An improved measure of stability can be obtained by modifying Equation [2] to include an energy dissipation term, ΔWD. This is the same component of energy referred to by Cook et al. (1966) who stated that the severity of rockbursts can be reduced by: ‘… increasing the dissipation of energy in a non-violent form.’

It would therefore be advantageous for the value of ΔWD to be as large as possible. A general measure of incremental mining stability, designated as ΔWR, can be defined as:

Figure 8—Geometry used to investigate the effect of total closure on APS and ERR

[4] The incremental stability measure, defined by Equation [4], is associated with each incremental change to the excavation shape.

It can also include released energy from explicitly modelled faults or other discontinuities. Napier and Malan (2018) proposed that a simple time-dependent limit equilibrium model could represent the fracture zone adjacent to the edges of tabular excavations.

In this case, the energy dissipation term ΔWD can be computed explicitly in a series of discrete time-steps with imposed face advance increments corresponding to a given mining schedule.

This approach allows parameters such as the size of mining increment and overall mining rate to be investigated.

Figure 9—Simulated area of total closure (green centre) for E = 10 GPa

The effect of total closure on ERR and APS As a simple illustration of the effect of total closure on ERR and APS, a remnant geometry was simulated with the TEXAN code (Napier and Malan, 2007). The geometry consisted of a single remnant of size 40 m × 40 m in a mined area of 400 m × 400 m (Figure 8). The remnant was offset to the left to allow a larger area of total closure on the right. This remnant was simulated at a depth of a 1000 m with various values of Young’s modulus to simulate different areas of total closure. The Poisson’s ratio in all cases was 0.2, the stoping width was 1 m, and the dip was assumed to be zero. A ‘stope’ constitutive model in TEXAN allows for the correct calculation of stress generated in areas

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Figure 10—Simulated stress regeneration in the area of total closure for E = 10 GPa The Journal of the Southern African Institute of Mining and Metallurgy

The need for improved layout design criteria for deep tabular stopes

Figure 11–APS of the remnant and the area of total closure for various values of E

Figure 12—APS of the remnant and ERR (adjacent to the large span) as a function of E

Figure 13—Simplified outline of the mining area simulated in TEXAN

APS on the remnant decreases from 174 MPa for no total closure to 119 MPa at the lowest value of E. Based on these elementary results, it is clear that elastic modelling may overestimate the stress acting on remnants in old mining areas with large mining spans if inappropriate values of E are used. Figure 12 illustrates the corresponding decrease in average ERR values for the remnant (calculated for the remnant face adjacent to the large area of total closure) once total closure occurs at low values of E. As a second modelling step using TEXAN, the actual layout in Figure 6 was simulated using different values of Young’s modulus. The layout outline was approximated using straightline segments to simply the generation of the triangular element mesh that was used for the simulation. The layout outline is shown in Figure 13 and the area simulated was approximately a 1000 m × 1650 m. An example of the triangular mesh is shown in Figure 14. The simulated APS results for remnant 16 (also shown inside the circle in Figure 6) are shown in Figure 15. Note that total closure for this simulated geometry occurs for values of The Journal of the Southern African Institute of Mining and Metallurgy

E < 50 GPa. The simulated APS value on the pillar is substantially reduced for low values of E because of the total closure.

Stress measurements to calibrate numerical models It appears from the modelling in the previous section that it will be difficult to accurately model pillar or remnant stresses in areas where total closure has occurred. The proposal by Gurtunca and Adams (1991) to use elastic modelling with a reduced Young’s modulus may be one approach to estimate APS values in remnant areas. In the short term, this may perhaps be more feasible than modelling attempts using complex inelastic codes to simulate fracturing, bed separation, and bulking of the rock mass. Calibration of these complex inelastic codes and forward modelling may possibly never be feasible owing to the large number of constitutive model parameters and the difficulty of characterizing the underground rock mass (Jing, 2003; Bahrani and Hadjigeorgiou, 2018). Further research is nevertheless required to investigate whether the gross simplification of a VOLUME 120

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Figure 15—Simulated APS of the remnant 16 (see Figure 13) as a function of E

Figure 14—Example of the triangular mesh surrounding remnants 11, 12, and 13 (see Figure 13)

stiffness reduction will lead to a better estimation of stress concentrations in remnant areas. Reducing the value of E has been discussed for many years as a method of reducing the disparity between underground closure and simulated closure, but the concept has also been widely criticized.

Owing to the difficulty in accessing old mining areas, an estimate of the area of total closure from visual observations may not be possible. A possible solution would be to do stress measurements in the footwall haulages below these areas and then use this data to calibrate the numerical models. Figures 16 and 17 illustrate the vertical stress along line AA’ (see the geometry in Figure 8) at depths of 50 m and 100 m below the reef. Note that the effect of the remnant is clearly visible at 50 m below the reef, but not at 100 m. The vertical stress in the

middle of the mined span increases substantially as the amount of total closure increases. In actual underground mines, stress measurements in the remnant areas will be important to verify the results obtained from numerical models. If there are areas of total closure, the measured vertical stress below these areas will typically be higher than the simulated values and the stress on nearby remnants on the reef plane will be lower. Both numerical modelling and actual stress measurements are required to quantify this effect.

The Fourth Industrial Revolution and rock engineering design criteria A further aspect to highlight is that, apart from seismic data, only limited data on the rock mass behaviour is currently collected in deep gold mines. It is hypothesised that the next major breakthrough in improved design criteria may be to combine the classical design criteria and in-situ rock mass data collected on a routine basis in the areas surrounding the active stopes.

Surprisingly, very little laboratory testing of rock properties is conducted for rock engineering purposes compared to, for

Figure 16—Vertical stress along line AA’ (Figure 8) at a depth of 50 m below reef for various values of E. For this geometry and parameters, total closure only occurs at values of E < 15 GPa

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Figure 17—Vertical stress along line AA’ (Figure 8) at a depth of 100 m below reef for various values of E. For this geometry and parameters, total closure only occurs at values of E < 15 GPa

example, the number of tests conducted during civil engineering construction. The so-called Fourth Industrial Revolution (4IR) and its effect on mining technology is discussed in recent popular articles (Odendaal, 2019). Four aspects may impact the mines of the future, namely automation, robotics, a digitally enabled workforce, and next-generation analytics and decision support.

A key building block of these technologies is new developments in sensor technology. The use of wireless sensor technology in rock engineering applications is an important development in this regard (Zhu and Zhuang, 2011) and large-scale application of this technology may significantly assist in the design of deep layouts. To illustrate the value of measurements other than seismic data, Figures 18 and 19 illustrates historical closure data that was collected in a deep gold mine in the Carletonville area (Malan, Napier, and Janse van Rensburg, 2007). The effect of preconditioning on the rock mass behaviour is clearly visible.

Routine rock mass behaviour data, such as this closure data collected on a mine-wide scale, would give managers and rock engineers a much improved understanding of aspects such as the effectiveness of preconditioning. This information cannot be obtained from the seismic networks and new developments in terms of wireless sensor technology may make this possible. It is recommended that research in this area be initiated.

Figure 18—The effect of preconditioning on the time-dependent closure of a deep tabular stope in the Ventersdorp Contact Reef. Note the increase in time-dependent closure after the first preconditioning blast (after Malan Napier, and Janse van Rensburg, 2007)

Conclusions This paper was compiled to highlight the need for additional research in the area of layout design criteria for deep gold mines.

In a recent article, Stacey (2019) wrote: ‘How is it possible that the mining industry would regard the research as too costly? And thus, how can rock engineering research capacity in South Africa have been allowed to dissipate completely from the research powerhouse that it once was?’ To further emphasise the need for additional research, this paper investigated problematic aspects related to two popular design criteria used in the deep gold mines of South Africa, namely average pillar stress (APS) and energy release rate (ERR). The early layout designs in the gold mines were based on trial and error and most of the recommendations of the 1924 Witwatersrand Rock Burst Committee are still valid in modern times. The introduction of APS and ERR assisted greatly in reducing areas of high stress concentrations. Both The Journal of the Southern African Institute of Mining and Metallurgy

Figure 19—The effect of preconditioning on the rate of steady-state closure for panels in the Ventersdorp Contact Reef (after Malan, Napier, and Janse van Rensburg, 2007)

criteria are of limited use, however, as it is not clear what the maximum value of APS on remnants and pillars should be and ERR has a significant drawback as no dissipative mechanisms are incorporated to allow for on-reef failure. A numerical modelling study has been described that illustrate the effect of total closure VOLUME 120

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The need for improved layout design criteria for deep tabular stopes on the simulated APS and ERR values of remnants. As an interim solution, it is recommended that many more actual stress measurements be conducted below remnant areas to supplement the numerical models. This may be a useful interim measure until more complex numerical models can be developed and calibrated to simulate inelastic rock mass behaviour on a mine-wide scale.

Further research is also required to investigate whether the gross simplification of a stiffness reduction in elastic models will lead to a better estimation of stress concentrations in remnant areas.

Reducing Young’s modulus has been discussed for many years in order to reduce the disparity between underground closure and simulated closure, but the concept has also been widely criticized. As a further important recommendation, routine rock mass behaviour data, such as closure data collected on a minewide scale, would give managers and rock engineers a much improved understanding of aspects such as the effectiveness of preconditioning. This information cannot be obtained from the seismic networks and new developments in terms of wireless sensor technology may make these large-scale measurements possible.

Acknowledgments This work forms part of a PhD study by Yolande Jooste at the University of Pretoria. This work was undertaken under the auspices of the Harmony Gold Chair of Rock Engineering, and the authors would like to thank Harmony Gold for permission to publish this paper. Professor John Napier is thanked for the assistance with the TEXAN code.

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The geotechnical evolution of deep-level mechanized destress mining at South Deep mine P.G. Andrews1, R.J. Butcher1, and J. Ekkerd2 Affiliation: 1 Gold Fields, Australia.

2 Gold Fields, South Africa.

Correspondence to: P.G. Andrews

Email:

peter.andrews@goldfields.com

Dates:

Received: 29 Jul. 2019 Accepted: 6 Sep. 2019 Published: January 2020

How to cite:

Andrews, P.G., Butcher, R.J., and Ekkerd, J. The geotechnical evolution of deep-level mechanized destress mining at South Deep mine.

The Southern African Insitute of Mining and Metallurgy DOI ID: http://dx.doi.org/10.17159/24119717/854/2020

This paper was first presented at the Deep Mining 2019 Conference, 24–25 June 2019 Misty Hills Conference Centre, Muldersdrift, Johannesburg, South Africa.

Synopsis The South Deep mine is a deep-level mine that is actively mining between 2600 m and 3000 m below surface, with expectations to mine to 3400 m depth. The The orebody lends itself to a fully mechanized mining method.

The main geotechnical challenges for successfully mining the South Deep orebody were to destress and then cost-effectively extract the massive, low-grade orebody. Since Gold Fields acquired South Deep in 2007, several mining methods have been used to date, but destressing was done conventionally using traditional South African narrow-reef gold mining methods. In 2015, the mine moved to high-profile (5.5 m high) horizontal destress development with mechanized installation of ground support, and crush pillars were replaced with yield pillars. This has resulted in a safer working environment with industry best-practice support standards and less seismic energy release, while still allowing appropriate productivity rates This paper outlines the geotechnical processes used to overcome issues as they were encountered, including ground support, seismicity, and rock mass conditions, and highlights the key leanings of a deep-level massive mine’s evolution over time.

Keywords mechanized mining, destress development, ground support, yield pillars, seismicity, backfill.

Introduction South Deep gold mine is situated approximately 45 km southwest of Johannesburg and 20 km south of Randfontein in the Far West Rand goldfield of the Witwatersrand Basin. The mining area covers 3563 ha and extends for 9.5 km north-south and 4.5 km east-west at its widest points.

Geology The geology at South Deep was described by Watson et al. (2014). The orebody lies within the Central Rand Group of the Witwatersrand Supergroup and is overlain by the Ventersdorp lavas (see Figure 1).

The Ventersdorp Contact Reef (VCR) and Upper Elsburg reefs are the most economically important units.

The Upper Elsburg reefs suboutcrop against the base of the VCR, which is a major stratigraphic unconformity. Towards the east, the orebody diverges and thickens up to about 120 m at the eastern extremity of the mine boundary, with an increasing percentage of non-profitable quartzite middlings in the thicker regions (see Figure 2). The targeted reef packages within the sequence are the EC and MB reefs. The dip and strike of the orebody varies across the mine, but it generally dips to the south at between 10° and 14°. This dip angle is too steep for normal mechanized equipment due to excessive loading on braking components.

The orebody is currently being mined at depths of between 2600 m and 3000 m, and future mining is planned at 3400 m below surface. The virgin vertical stress is high and will become higher as the depth increases.

Mining Over the years, several different mining methods have been used at South Deep to maximize extraction of the large orebody, while trying to ensure that these are the safest and most cost-effective methods available. Since Gold Fields acquired the mine in 2007, several methods have been trialled and abandoned. This optimization work has been an ongoing process as the level of knowledge and confidence in each of the methods increased.

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The geotechnical evolution of deep-level mechanized destress mining at South Deep mine

Figure 1—Simplified 3D section showing the stratigraphy around the strategic reefs

Figure 2—Generalized east-west section showing the stratigraphy of the orebody, targeted reef units, and corridor geometry

Table I

In-situ stress field used for numerical modelling purposes (SRK, 2009) Principal stress component s1 s2 s3

Magnitude gradient (MPa/km) 27.0 24.0 6.0

The underlying principle of all methods is that a narrow tabular cut is mined first. This destresses the orebody directly above and below the cut, ensuring the safest and most costeffective extraction of the orebody.

The destressing philosophy will be discussed first, followed by a description of the various mining methods used by Gold Fields for the extraction of the destress mining layouts.

Extraction sequences for the massive mining will then be discussed. Backfill plays an integral role in all the methods, and the backfill practices for each method are discussed.

Geotechnical environment Rock mass conditions Most of the conglomerate units within the Upper Elsburg package are extremely strong, brittle rocks. SRK (2006) identified three rock strength classes within the Upper Elsburg sequence. ➤ Argillaceous and gritty quartzites are the lower strength parts of the package, although they are still generally strong, with σc ranging from 80 to 150 MPa.

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Dip

Dip azimuth

si/szz

90 00 00

000 000 = 180 090 = 270

1 0.89 0.59

➤ Medium-strength, sub-argillaceous to siliceous quartzites with σc ranging from approximately 150 to 200 MPa. ➤ High-strength siliceous conglomerates and quartzites with σc > 200 MPa. The RMR within the reef package is variable but generally high, with many RMR values over 80. Based on geotechnical logging and mapping, the geological strength index (GSI) ranges from 55 to 75.

Stress field The virgin stress tensor used in the modelling was determined from stress measurements using CSIR cells, carried out at a depth of 2650 m below surface. SRK (2009) provides the in-situ stress field as summarized in Table I.

Destress philosophy In high-stress environments, the rocks of the Upper Elsburg package store strain energy that can be released violently in the form of rockbursts. The solution was to mine a narrow tabular cut, which would destress the orebody above and below to allow The Journal of the Southern African Institute of Mining and Metallurgy

The geotechnical evolution of deep-level mechanized destress mining at South Deep mine normal massive mining techniques to be conducted (James, MacDonald, and Raffield, 1998). Initially, conventional destress cut mining was used at South Deep as this method is used extensively at depth in South Africa.

Early elastic numerical modelling work indicated that an optimum conventional destressing cut could be achieved with a stoping width of 1.5 m to 2.0 m and backfilling. To ensure that the average energy release rates (ERR) were maintained below the mining-industry accepted average criterion of 30 MJ/m2 (Jager and Ryder, 1999), the panels in the destress cut were limited to 250 m, separated by 60 m wide stability pillars. Later modelling showed that the vertical stress could be reduced to approximately 30 MPa to 40 MPa up to 60 m above and below the destress cut (see Figure 3). The destress envelope that was created had stresses one would expect at a depth of 1 200 m.

Backfill is therefore the most crucial support element. The destress cuts had to be backfilled to limit bedding separation in the hangingwall and provide areal support and energy absorption capacity.

Although mining the destress cuts conventionally worked well, the rate of destress advance was slow and did not open up enough of the mining areas to achieve the required production rates for longhole stoping.

Mechanized destress mining history Mechanized apparent dip destress mining The use of the mechanized apparent dip destress method started in 2008. This was the first mechanized destress method implemented and it involved mining a combination of apparent dip drives to excavate the target destress cut horizon in the plane of the reef (see Figure 4). Development was 2 m high and 4 m wide. The concept was to mimic conventional destressing using mechanized equipment.

Ground support systems Hangingwall support initially comprised 46 mm diameter friction bolts and 5.6 mm gauge weldmesh. Friction bolts were phased out and replaced by more robust flexi-anchors as an interim measure. It was planned to eventually replace the anchors with yielding reinforcement to provide the necessary energy absorption requirements.

Once the stoping drives were completed, they were backfilled with cemented classified tailings (CCT). There was no need to construct a backfill paddock as the bulkheads were formed using POWERITE™ bags filled with CCT. Backfill was contained by the two bulkheads, previously placed backfill, and the rock face.

Figure 3—Sectional view of the destress concept. The vertical stress trajectories are shown in red

Figure 4—Oblique view of the mechanized apparent dip destress mining method The Journal of the Southern African Institute of Mining and Metallurgy

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The geotechnical evolution of deep-level mechanized destress mining at South Deep mine Seismicity The ERR associated with this layout are equivalent to those of conventional mining with the same regional pillar configuration.

In principle, the destressing effect was presumed to be identical to that achieved with conventional destressing. Larger events clustered around geological structures and stabilization pillars, while smaller events (ML 0.0 to ML 2.0) were concentrated around the destress mining front.

Issues and method change Apparent dip mechanized mining of the destress slots was discontinued in late 2008. Issues with the method included: ➤ Difficulty in maintaining development on the correct ore horizon, especially when encountering faults ➤ Delays in backfill and difficulties in getting a tight fill against the hangingwall with bags ➤ Ground support was still installed conventionally, as mechanized bolting was not viable due to the narrow mining heights. There was very little synergy with massive mining, and no shared development.

Low-profile mechanized destress mining (horizontal destress)

The horizontal destress method with no pillars was used from 2009 to 2012. This method involved mining layered horizontal destress cuts. These horizontal cuts overlap and destress the target mining horizons (see Figure 5). Access to the destress cut was via a spiral decline located beneath a previously mined area and which was always destressed. Access drives were developed horizontally from the spiral decline to each horizontal destress horizon. The mining corridor width of 240 m between stability pillars was maintained. There was a 17 m vertical spacing between destress cuts.

Each horizontal cut was mined as a series of perpendicular drives, which used set leads and lags to mitigate potential rockburst and stress damage. All development was planned at 5.0 m wide and 2.2 m high (Figure 6). Main access drives (MADs) were developed in the dip direction (Watson et al., 2014). Stoping drives (SDs) were mined adjacent to the MADs on a modified drift-and-fill sequence. Strike access drives (SADs) were created on strike every 10 m by placing backfill in the SDs. This ensured that cross-fracturing in the SAD hangingwall was avoided. The plan was to integrate this destress cut and subsequent longhole stoping by utilizing the original destress cut drives. These drives would be filled after the destress cut was completed. Redeveloping through the fill was required with backs stripping or footwall slipping being undertaken to create the final dimensions of the longhole stoping access drives, designed to be at least 5.0 m high by 4.5 m wide.

Figure 5—Cross-section of the horizontal destress layout

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This mechanized horizontal destress method had several perceived advantages over the apparent dip destress method (Joughin, Bester, and du Plooy, 2011), including: ➤ Development layout synergies with the subsequent massive mining ➤ The ramp infrastructure can be utilized for massive mining ➤ The destress cut access and strike drives could be utilized for subsequent longhole stoping ➤ Higher grade ore from destressing development ➤ Adjustments to the layout are not required to accommodate geological structures, as would be the case with an apparent dip layout ➤ Having the destress within the trackless target horizon maximizes the destress envelope ➤ Multiple longhole stopes can be stacked on top of each other to form one large stope.

Ground support systems Ground support continued to use the 46 mm diameter friction bolts and 5.6 mm gauge weldmesh in the development in existing destress shadows. Destress development into higher stress abutments used the Flexibolt, a yielding anchor from M&J Mining. Dynamic yield was achieved by dragging 300 mm of cable, located at the back of the anchor, through a small orifice at the position of the anchor. However, yielding bolts were still installed and tensioned conventionally.

Ground performance Overall, rock mass conditions post-extraction were considered fair. If backfill was placed on time and installed tight against the hangingwall, then hangingwall conditions were good.

Often, backfill was installed late or not installed tight against the hangingwall. This caused the hangingwall conditions to deteriorate, with opening along bedding planes and large-scale convergence up to about 500 mm.

Production The horizontal destress method was used from 2009 to 2012.

Production was approximately 1.4 Mt in 2009, 1.75 Mt in 2010, and 1.98 Mt in 2011. During these years about 56% of total tonnage came from development (destress, normal, and new mine development) and 44% from some form of stoping (longhole, or drifting and benching). The large amount of development was to set up stoping areas in the future.

Figure 6—Plan view of the horizontal destress layout The Journal of the Southern African Institute of Mining and Metallurgy

The geotechnical evolution of deep-level mechanized destress mining at South Deep mine Seismicity Seismic activity averaged around 40 events > ML 0.0 per month.

Larger events clustered around pillars, while smaller events (ML 1.0 to ML 2.0) were concentrated around the destress mining front. There was a moderate correlation between the number of events per month and production. The monthly seismic energy released was between 5 MJ and 180 MJ, with the average being about 45 MJ/month.

Issues and method change The mechanized horizontal destress method was used between 2009 and 2011. During this time, several issues were noted with the method, including: ➤ Difficulty in maintaining 90° turnouts on the destress horizon. Turnouts became large and required more backfill bags to reduce spans ➤ Difficulties mining on-line and on-grade ➤ Delays in backfill placement, which caused delays in development and future stoping ➤ Difficulties when tight-filling to the backs with backfill bags ➤ Convergence in the centre of the destress cuts (approx.

500 mm). This was exacerbated by backfilling delays ➤ Bags provided support only when closure occurred.

Due to issues with backfilling delays and convergence, the method was changed to a horizontal destress method with crush pillars. Trials of the method began in 2012 and it was fully adopted in 2013.

Low-profile mechanized destress mining with crush pillars (LPS) This method involved the addition of crush pillars in the horizontal destress cuts. The crush pillar concept incorporates pillars intended to crush while they are still part of the face, but which have sufficient residual strength to provide the required support resistance to the immediate hangingwall, both at the face and in the back areas. These pillars can yield over a large deformation range at their residual strength level (Ozbay, Jager, and Ryder, 1995).

The LPS method was utilized for three years from 2012 to 2014. Access to the destress cut was still obtained via a spiral decline, and access drives still developed horizontally from the

spiral decline to each horizontal destress horizon. The mining corridor width was maintained at 240 m between stability pillars with 17 m vertical spacing between destress cuts.

There were many expected advantages from the transition to LPS, as documented by Watson et al. (2014). These included: ➤ Less backfill required ➤ More heading availability ➤ The crush pillars are an active support system and would inhibit hangingwall unravelling, which is often observed where the reef is replaced by backfill.

The destress development still used the 5.0 m wide and 2.2 m high design. Main access drives were first developed in the dip direction (see Figure 7). SDs were then mined adjacent to the MADs in a staggered configuration to maintain acceptable lead-lag distances. This distance was originally designed to create a 1.5 m wide crush pillar, but was expanded to a 2.0 m wide crush pillar. Stope access drives (SADs) were developed at 15 m spacing by cutting 5 m holings through the crush pillars at the appropriate locations. PoweRite™ backfill bags were installed along the edges of the crush pillars to confine the pillars and reduce pillar disintegration at large closures.

Ground support systems Ground support in the destressed rock mass used the 46 mm diameter friction bolts and 5.6 mm gauge weldmesh in the development. The sidewalls of the MAD were also supported with friction bolts and weldmesh. The hangingwall of the destress development into higher stress abutments was supported with 5.6 mm diameter weldmesh and yielding tendons. Yielding bolts continued to be installed and tensioned conventionally. Many of the Flexibolts could not be installed properly as correct tensioning of the bolts could not be undertaken. This allowed bolts to loosen and slip, causing further dilation of the hangingwall.

Ground performance Rock mass conditions within the destress cut post-extraction were considered poor. Crush pillars deteriorated quicker than expected and provided very little post-peak load capacity (see Figure 8a). Late placement of backfill saw hangingwall deterioration with large openings in bedding planes occurring (Figure 8b). Convergence was high, with closure greater than 500 mm in many of the destress cuts.

Figure 7—Plan view of the horizontal destress with crush pillar layout The Journal of the Southern African Institute of Mining and Metallurgy

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The geotechnical evolution of deep-level mechanized destress mining at South Deep mine month. The increase in the energy released and the number and magnitude of events is attributed to the high levels of convergence caused by the crush pillars over-crushing and losing all load-bearing capacity (see Figure 9).

Issues and method change

Figure 8—(a) Deteriorated crush pillar in LPS (b) Opening of bedding planes in LPS

Production The first two years’ production was very good, with over 2.05 Mt extracted in 2012 and 2.24 Mt in 2013. However, due to poor ground conditions and the required remediation, there was a significant decrease in tonnage in 2014 with only 1.09 Mt extracted. During these years, some 60% of the total tonnage came from development and 40% from stoping.

As can be seen from the production results, the reduction in the need for backfill saw a significant increase in extraction rates for two years. Backfill fell further behind requirements as significantly more bags to fill at more locations were required by the new method. This lack of backfill reduced pillar confinement, which allowed pillars to fret away causing large convergence within the destress cuts. The large levels of convergence prevented productive mining due to the extra rehabilitation and backfill requirements.

Seismicity During 2012 and 2013, seismic activity averaged around 60 events > ML 0.0 per month. While mining with the LPS method, the number of events of magnitude ML > 2.0 increased from a historical average of one per month to 1.8 per month. There was a moderate correlation between the number of events per month and production.

From January 2012 to March 2013, the monthly seismic energy released was between 5 MJ and 90 MJ, with the average being some 30 MJ/month. However, from April 2013 to January 2014 the monthly seismic energy released increased to between 40 MJ and 200 MJ, with the average being some 100 MJ/

Several issues with the method were noted, including: ➤ Convergence in the centre of the destress cuts of about 500 mm as crush pillars deteriorated. This created very poor working conditions ➤ The convergence was accelerated by backfilling delays ➤ Difficulty in maintaining 90° turnouts on the destress horizon, as turnouts became large and required more backfill bags to reduce spans ➤ Difficulties mining on-line and on-grade ➤ Delays in backfilling, which caused delays in development and future stoping. ➤ Difficulties when tight-filling to the backs with backfill bags.

After the poor performance in early 2014, Gold Fields management instated a geotechnical review board to review and comment on the LPS mining method. The results of the review indicated that the LPS method was flawed owing to the issues noted above. The report outlined many short-term remediation strategies, including flood-filling certain areas to control convergence and a regime to catch up on the backlog of secondary support. These recommendations were adopted and impacted on the 2014 production figures.

The main recommendations for the medium to long term were to continue using the destress method but move to a larger development profile to ensure mechanized support installation.

Pillars were to be larger but still designed to yield to prevent pillar bursts. An investigation to consider a reduction in the corridor spans to control convergence was also recommended.

High-profile mechanized destress mining with yield pillars (HPS) During late 2014 and early 2015, much work was done on the recommendations provided by the geotechnical review board.

Many areas were flood-filled based on rock mass conditions, and production was further impacted due to the work on the secondary support backlog.

Figure 9—Seismic energy versus production for the various mining methods

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The geotechnical evolution of deep-level mechanized destress mining at South Deep mine The outcome of this work was the creation of the high-profile mechanized destress mining method with yield pillars (HPS).

Further work was done on detailing the new method, followed by numerical modelling of these new designs.

Yield pillars are pillars which are intended to have a safety factor > 1 or even equal to unity when first formed, but then yield in a stable manner at stress levels near to peak strength (Ozbay, Jager, and Ryder, 1995). The pillars are sized, often using empirical and observational approaches, so that they do not store excessive strain energy and burst, but remain sufficiently intact to maintain a residual strength. In practice, yield pillars are intact when formed and are then weakened as the load on them increases beyond their load-bearing capacity, causing them to yield.

The new method involved the following changes. ➤ Increasing the destress development profile to 5.0 m high and 4.5 m wide to allow for mechanized support installation. ➤ Changing the crush pillars to yielding pillars. The original size of the pillars was to be 6 m wide by 10 m long, but this was changed to 8 m wide by 20 m long yielding pillars in 2017 (see Figure 10). ➤ Mechanized ground support changed to include dynamic support as primary support. ➤ Reduction of corridor spans to 180 m. This was based on numerical modelling results to ensure ERR were below industry requirements. ➤ The destress cut layout was changed to a herringbone configuration to reduce the number of both 90° turnouts and four-way intersections. The 17 m vertical spacing between destress cuts was maintained.

The method was trialled in early 2015. From mid-2015 to mid-2016, the mine used both LPS and the new HPS method.

By mid-2016, all new destress cuts were mined as per the new design. In 2017, a modification to the design to incorporate a trial of 8 m wide by 20 m long yield pillars was undertaken. This new design also had twin MAD access and allowed for workshops on each destress level.

1.24 Mt, increasing again in 2016 to 1.72 Mt. The change to larger yield pillars in 2017 saw production remain consistent with 1.61 Mt extracted. During these years, some 55% of the total tons came from development.

Seismicity Since the inception of the HPS method, monthly seismic energy released has been between 5 MJ and 60 MJ, with the average being < 20 MJ/month. The reduction in the energy released and the number and magnitude of events is attributed to the reduced levels of convergence. Larger events still cluster around stability pillars and on major structures, while smaller events (ML –1.0 to 2.0) are concentrated around the destress mining front.

Further details on seismicity at South Deep are given in the companion paper.

Conclusion Over the years, South Deep mine has made considerable progress in developing a fully mechanized mining method to extract the extensive orebody both laterally and vertically. All methods have taken advantage of the destress philosophy, which incorporates a tabular mining cut within the target package to destress the rock mass above and below the cut.

Mining methods have changed over the years from a low-profile apparent dip destress method without pillars to a horizontal low-profile method, initially without pillars but subsequently using crush pillars as part of the sequence (LPS).

Due to the inability to perform mechanized support installation and poor rock mass conditions associated with the LPS methods, a high-profile method with mechanized support installation and large yield pillars was adopted.

Ground support systems Ground support in the destressed rock mass used the Garock Hybrid™ dynamic friction bolt and 5.6 mm gauge weldmesh in the development. The support scheme was installed across the backs of the drive and down the sidewalls to 1.5 m from the floor at 1.4 × 1.2 m spacing. Cable bolts were to be installed in intersections and at longhole stope brows. All ground support was mechanically installed. In 2018 shotcrete was added to the support scheme to reduce pillar unravelling below the mesh line.

Ground performance

Figure 10—Oblique view of the latest mechanized high-profile (HPS) destress mining method with larger yield pillars

The HPS method resulted in an immediate improvement in rock mass conditions during the development of the destress cut. Less convergence was observed, with a maximum of about 200 mm.

Over the first 18 months of the HPS trial, it was observed that the 6 m wide by 10 m long pillars were yielding to the point of crushing. The highly fractured rock mass in the yield pillars was unravelling below the mesh line, causing the further reduction in pillar size. Rock mass conditions further improved with the larger pillars, which are in use today (see Figure 11a and Figure 11b).

Production The production rate built up from the 2014 low of 1.09 Mt to The Journal of the Southern African Institute of Mining and Metallurgy

Figure 11—(a) HPS development with bolts and mesh (b) HPS development with fibrecrete VOLUME 120

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The geotechnical evolution of deep-level mechanized destress mining at South Deep mine The latest HPS method creates a safer working environment with industry best-practice support standards and releases less seismic energy than the LPS methods, while still allowing appropriate production rates.

The South Deep rock engineering team has used an observational method to continually assess ground conditions, pillar stability, and development convergence to allow ongoing optimization and changes to each method, culminating in the current HPS method with large yield pillars.

Acknowledgements The authors would like to acknowledge the South Deep management team for their assistance in writing this paper.

References Jager, A.J. and Ryder, J.A. 1999. A handbook on rock engineering practice for tabular hard rock mines. Safety in Mines Research Advisory Committee (SIMRAC), Johannesburg.

James, J.V., MacDonald, A.J., and Raffield, M.P. 1998. The backfilling philosophy for massive mining at depth in the South Deep Section, Western Areas Gold Mine.

Proceedings of the Sixth International Symposium on Mining with Backfill.

Bloss, M.L. (ed.). Australasian Institute of Mining and Metallurgy, Melbourne.

Joughin, W.C, Bester, W.M., and du Plooy, M. 2011. Mining methods and backfill at South Deep Gold Mine. Minefill 2011. Proceedings of the International Conference on Mining with Backfill. Southern African Institute of Mining and Metallurgy, Johannesburg.

Ozbay, M.U., Jager, A.J., and Ryder, J.A. 1995. The design of pillar systems as practised in shallow hard-rock tabular mines in South Africa. Journal of the South African Institute of Mining and Metallurgy, vol. 95, no. 1. pp. 7–18.

SRK. 2006. South Deep regional pillar optimization study (Stage II). Report 352262/2 to South Deep. February 2006.

SRK. 2009. Rock engineering assessment of proposed horizontal destress cuts and massive mining at South Deep. Report 396956/1 to South Deep. February 2009.

Watson, B.P., Pretorius, W., Mpunzi, P., du Plooy, M., Matthysen, K., and Kuijpers, J.S. 2014. Design and positive financial impact of crush pillars on mechanized deep-level mining at South Deep gold mine. Journal of the Southern African Institute of Mining and Metallurgy, vol. 114, no. 10.. pp. 863–873. u

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The role of rock mass heterogeneity and buckling mechanisms in excavation performance in foliated ground at Westwood Mine, Quebec L. Bouzeran1, M. Pierce2, P. Andrieux3, and E. Williams4 Affiliation: 1 Itasca Consulting Group Inc. (ICG), United States.

2 Pierce Engineering, United States.

3 A2GC, Canada.

4 CVM Consultants, Mississauga, Canada.

Correspondence to: L. Bouzeran

Email: lbouzeran@itascacg.com

Dates:

Received: 31 Jul. 2019 Accepted: 3 Oct. 2019 Published: January 2020

Synopsis Operations at Westwood mine in Quebec, Canada were temporarily halted in May 2015 after three largemagnitude seismic events occurred over two days. The mechanisms leading to these events, which caused severe damage to several accesses, were not well understood at first. This is partly due to the complex geology at the site, where massive, unaltered, strong, brittle, and seismically active rock can alternate with highly altered, weak, foliated, and buckling-prone rock at the metre scale. Other aspects of ground behaviour, such as the significant discrepancy in blast-hole performance between secondary and primary stopes and the propagation of damage from stopes to haulage drives in some locations, were also not well understood. In 2017, further geotechnical characterization of the rock mass was carried out and numerical back-analyses of several locations were completed using the continuum code FLAC3D. The objectives of the back analyses were to better understand the mechanisms controlling rock mass performance and to obtain a calibrated model for predictive stoping simulations. This paper presents the key aspects of the modelling, which include (1) an anisotropic rock mass strength model with properties derived from field and laboratory strength testing, and (2) a scheme to account implicitly for the deconfinement that accompanies buckling around excavations.

Keywords rock mass performance, anisotropy, back-analysis, FLAC3D, deconfinement, buckling.

How to cite:

Bouzeran, L., Pierce, M., Andrieux, P., and Williams E. The role of rock mass heterogeneity and buckling mechanisms in excavation performance in foliated ground at Westwood Mine, Quebec.

The Southern African Insitute of Mining and Metallurgy DOI ID: http://dx.doi.org/10.17159/24119717/860/2020

This paper was first presented at the Deep Mining 2019 Conference, 24–25 June 2019 Misty Hills Conference Centre, Muldersdrift, Johannesburg, South Africa.

Introduction The Westwood mine is located approximately 80 km west of the town of Val-d’Or in Quebec, Canada.

Operations at the mine were halted by three large-magnitude seismic events that occurred over two days in May 2015 (Kalenchuk, Mercer, and Williams, 2017). In addition to these events, which caused severe damage to several accesses, other aspects of ground behaviour, such as the significant discrepancy in the performance of blast-holes between secondary and primary stopes and the propagation of damage from stopes to haulage drives in some locations, were not well understood. In order to better understand the rock mass behaviour and its impact on the stability of excavations, additional geotechnical characterization of the rock mass was carried out and back-analyses of several locations were completed to calibrate a rock mass model to be used for forward analyses. The modelling work described in this paper was performed in three key steps. ➤ Defining an anisotropic rock mass model with the CaveHoek constitutive model (Pierce, 2013), with matrix and foliation properties derived from field (point load testing or ‘PLT’) and laboratory testing, and rock mass strengths derived from virtual testing of Ubiquituous Joint Rock Mass (UJRM) numerical specimens (Clark, 2006; Sainsbury, Pierce, and Ivars, 2008). ➤ Understanding the fundamentals of the buckling mechanism in buckling-prone rock at Westwood through discrete modelling using the distinct element code 3DEC (Itasca, 2016), representing foliation explicitly and using point load data directly to populate matrix and foliation properties at the drift scale. This allowed the implementation of a ‘buckling scheme’ in the continuum code FLAC3D (Itasca, 2017) to capture, in a continuum, the effect of the buckling observed in 3DEC in terms of deconfinement and stress redistribution at the drift scale (single drift and stacked drifts configuration). ➤ Simulating three case studies in FLAC3D involving performance at different scales (raise bore, drift, stope, multiple levels) to further refine the understanding and the modelling of the rock mass at Westwood.

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The role of rock mass heterogeneity and buckling mechanisms in excavation performance Site description The Westwood mine is located on the Doyon property and on the 120 km line separating Rouyn-Noranda from Val-d’Or, on which are located a number of producing and past-producing mining projects such as Bousquet and LaRonde. Commercial production started in July 2014. The orebody consists of multiple veins (often close to one another) of narrow thickness (0.1 m to more than 10 m) and striking E-W, as is the foliation when present.

The foliation typically dips at around 75 degrees. The geology is complex, with the presence of massive, unaltered, strong, brittle, and seismically active rock as well as highly altered, weak, foliated, buckling-prone rock as shown in Table I (IAMGOLD 2017a). A mixture of strong and weak rock can be encountered at the drift scale. The rock mass behaviour is time-dependent and exhibits a strong anisotropy due to the foliation. There is high horizontal stress that is likely oriented oblique to the orebody.

Stopes are typically 30 m high and 13 m long. Depending on the constraints, both hangingwall or footwall access is employed and both primary/secondary sequences and pillarless sequences are used.

The locations selected for case study analysis are shown in Figure 1. These areas were back-analysed numerically in order to understand the rock mass behaviour and key mechanisms at play at Westwood and to calibrate the model to ensure they are captured correctly. Case study ‘104’ refers to ‘the 104-00 area and was selected as this portion of the ramp (colloquially referred to as the ‘Coke Can’) had experienced extensive damage prior to and after three large seismic events (far from any stoping activity) which led to severe damage and a temporary shutdown of the mine. Case study ‘132-03’ refers to the 132-02 to 13204 TB S-W area and was selected due to significant damage (and high convergence) induced by stoping in crosscuts, which extended to the haulage accesses. Finally, case study ‘230’ refers to the 132-03 to 132-10, Z230-C area and was selected due to intense hole squeezing problems and local microseismic activity in a particularly high-stress environment. It should be noted that a reduced version of the 104 case study used for calibration

Table I

haracteristics of the different units encountered at C Westwood mine (modified from IAMGOLD, 2017a) Type A B C D D2 E E2 F

Nature

Convergence

Microseismicity

Shaly Shaly Shaly Shaly/massive Shaly/massive Massive Massive Massive

Very strong Strong/very strong Strong Variable Variable Weak/average Weak Weak

Weak Weak Weak Variable Strong/very strong Average Strong/very strong Strong/very strong

purposes, (mostly due to computation requirements) is presented here (referred to as the ‘simple 104 model’). A large-scale 104 area model was built to analyse the three large seismic events but is not presented in this paper.

Rock mass representation Characterization of matrix and foliation strength In order to better understand the variability in matrix and foliation properties within and across the different domains, a systematic point load test campaign was carried out on core from five new boreholes distributed along strike and depth. With boreholes oriented near perpendicular to foliation, it was possible to obtain a measure of the matrix strength from axial tests and foliation strengths from diametral tests (with foliation aligned appropriately with the testing direction as specified in the ISRM standard). A short 0.5 m spacing was employed between tests to obtain a highly detailed picture of foliation and matrix Is(50) variability by domain (see example in Figure 2). The point load indices were converted to uniaxial tensile strengths according to the ISRM standard (uniaxial tensile strength = Is(50)/0.8) and Weibull distributions generated fit to the matrix and foliation strengths by domain. The matrix point load indices were also correlated with UCS results from laboratory testing.

Figure—Locations of the case studies at Westwood mine (modified from IAMGOLD 2017b)

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The role of rock mass heterogeneity and buckling mechanisms in excavation performance

Figure 2—Example of foliation and matrix point load indices along a drill-hole used to generate Weibull distributions of direct tensile strength (one colour per unit)

Figure 3—Left: Ubiquitous joint rock mass specimen with explicit strength distribution. Right: UCS test of UJRM for various foliation orientations

For each unit, the uniaxial tensile strength distributions derived from point load testing were used to populate matrix and foliation strengths at the zone level in large (40 m high) UJRM specimens of rock using FLAC3D. An example specimen is shown in Figure 3. A UJRM specimen can be assigned separate matrix and foliation strengths to obtain anisotropic mechanical behaviour and makes use of the CaveHoek constitutive model (Pierce, 2013) with embedded ubiquitous joints. The CaveHoek constitutive model is able to simulate the strain-softening behaviour and uses the Hoek-Brown envelope (Hoek, CarranzaTorres, and Corkum, 2002) for the peak and residual strengths.

It also allows for representation of modulus softening, density adjustment, dilation, dilation shutoff, scaling of properties to zone size, cohesion weakening, tension weakening, and frictional strengthening. It has been adapted to account for anisotropy and buckling in the course of this work through the addition of ubiquitous joints (Clark, 2006; Board and Pierce, 2009) and an ad-hoc buckling scheme. The ubiquitous joint model is an option in the FLAC3D CaveHoek constitutive model that accounts for the presence of closely spaced planes of weakness. The criterion for failure on the plane, whose orientation is given, consists of a composite Mohr-Coulomb envelope with tension cut-off and brittle cohesion weakening.

Since the point load testing campaign only provides an estimate of uniaxial tensile strength, the corresponding cohesion had to be assumed. For the Westwood UJRM samples, the ratio between the foliation cohesion and tensile strength was assumed to be 2.5, based on experience. The friction angle was The Journal of the Southern African Institute of Mining and Metallurgy

set at 25 degrees to account for the smooth, planar nature of the foliation surfaces. The Hoek-Brown m parameter for the matrix was assumed to be equal to the ratio between the matrix uniaxial compressive strength (from laboratory testing) and the matrix uniaxial tensile strength (from point load testing). The UJRM specimens are tested numerically in uniaxial compression to obtain an estimate of the large-scale strength of the rock mass according to test direction relative to the foliation. These emergent large-scale strengths are lower than the input matrix and foliation strengths since the lower end of the input strength distribution (the weaker zones) tends to control the failure of the sample. Figure 3 shows results of UCS tests performed on UJRM specimens of each geotechnical domain for different foliation orientations. The maximum strength is obtained when the test is performed perpendicular to the foliation and failure is controlled by matrix failure, while the minimum strength is reached when the test is performed between 45 and 60 degrees from the foliation direction and failure is controlled by shearing on the foliation. Table II gives the zone-scale (input) and large-scale (emergent) properties of UJRM specimens for matrix-controlled failure and foliation-controlled failure.

Calibration parameters

The large-scale rock mass strengths derived from UJRM testing were used in the numerical back-analysis of tunnel and stope performance. Some adjustments were generally required to achieve the best match to monitored performance. This calibration was completed mainly through the adjustment of the rock mass strength index (SI), which adjusts the peak strength VOLUME 120

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The role of rock mass heterogeneity and buckling mechanisms in excavation performance Table II

B ase case zone-scale (input) and large-scale (emergent) properties of UJRM specimens for matrix-controlled failure and foliation-controlled failure (strengths in MPa) Scale

Parameter

Unit A

Unit C

Unit D

Unit D2

Unit E

Unit E2

Unit F

Matrix-controlled failure Zone-scale (input)

Weibull shape parameter

2.5

3.0

4.4

3.2

3.7

6.7

4.1

Characteristic tensile strength

5.9

8.2

11.0

10.3

11.0

10.4

11.4

(Sig_ci/TENS) ratio

10 9.7 10 10.4 9.6 10.2 10.4

Mean sig_ci value

66

89

126

121

119

123

136

Compressive strength

25

36

59

49

52

68

62

Large-scale (emergent)

Tensile strength

(UCS/TENS) ratio

3.0 4.4 6.9 5.7 6.3 7.6 7.0 -8.4 -8.3 -8.5 -8.7 -8.3 -9.0 -8.9 Foliation-controlled failure

Zone-scale (input)

Weibull shape parameter

1.7

1.2

1.4

1.6

2.0

1.2

1.5

Characteristic tensile strength

0.6

2.0

2.3

4.2

7.0

1.7

3.2

(COH/TENS) ratio

Large-scale (emergent)

2.5 2.5 2.5 2.5 2.5 2.5 2.5

Mean cohesion value

1.7

6.0

6.5

11.9

19.6

4.9

Compressive strength

3.1

7.0

9.1

14

22

7.2

12

Tensile strength

0.4

0.9

1.2

2

3

0.9

1.6

Cohesion

(COH/TENS) ratio

2 4.4 5.8 8.9 14 4.5 7.5 5.1 4.9 4.8 4.7 4.7 5.0 4.7

of the rock mass compared to the large-scale emergent strengths from UJRM testing, and the rock mass brittleness index (BI), which controls the degree of brittleness of the matrix.

The degradation of the peak strength envelope is done in a similar fashion as when considering GSI to account for the presence of joints at large scale in a rock mass behaviour (Hoek, Carranza-Torres, and Corkum, 2002). Hence, the rock mass SI can vary from 0 to 100, where 100 corresponds to the large-scale emergent strengths from UJRM testing. The range for SI for the matrix typically used in this project for Westwood rock units and the different case studies is 95 to 100. The need to degrade the rock mass properties from UJRM testing to achieve a calibrated model can be attributed to several factors, including: ➤ The presence of medium-scale features not accounted for in the UJRM samples ➤ The variability of local rock mass strength and presence of weaker ground at the location of the case studies ➤ Bias in the selection of UCS samples (by selecting ‘nice’ samples), which would tend to overestimate the rock mass compressive strength.

The rock mass brittleness is adjusted through the critical strain parameter in the CaveHoek constitutive model. The critical strain is the critical plastic shear strain (epscrit) required to reach residual strength after the peak strength has been reached.

If the critical strain is zero, the rock is perfectly brittle, i.e., it reaches residual strength as soon as it yields. If the critical strain is very large, the rock is very ductile, i.e., its strength decays very slowly from peak values after it starts yielding. The critical strain of a rock mass is an important parameter but is difficult to evaluate. At Westwood, the value of the critical strain has been calibrated through the variation of the brittleness index (BI). The critical strain is estimated as shown in Equation [1]. This type of estimation was first determined through a back-analysis by Lorig and Pierce (2000) of rock mass failure in caves and other openings as part of the International Caving Study, and provides

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a starting point. Thus, if BI = 100, the rock mass is perfectly brittle. If BI = 0, the rock mass is assigned a very large critical strain and essentially behaves in a perfectly plastic fashion. The presence of zone size (d) within this relationship recognizes that the critical strain is zone-size dependent in continuum models, where shearing tends to be resolved in a band approximately one zone thick. When using the CaveHoek constitutive model with ubiquituous joint functionality, the critical strain needs to be defined for the matrix and for the joints. In this project, ‘joints’ representing the foliation are considered perfectly brittle, which means that the critical strain is null, and so as soon as they reach the peak strength, their cohesion is downgraded to zero. The matrix strength, however, is considered less brittle, and the value of the index has been calibrated for each case study. The range for BI for the matrix typically used for Westwood rock units in this project is 80 to 92. This gives a critical strain of 1 to 2.5% for a 1 m zone.

Critical strain = (12.5 – 0.125 × BI)/(100 × d) [1]

The base case rock mass strength index is 100, which corresponds to the direct use of the large-scale rock mass strengths from UJRM testing. The base-case rock mass brittleness index is 80, which corresponds to a critical plastic shear strain for the matrix of 2.5%. Other base case parameters for each unit are listed in Table II.

Stress state

The in-situ stress measurements at Westwood are quite variable, both in terms of direction of the principal stresses and the ratio between principal stress magnitudes. Based on field measurements and early simulations of this project, it was decided to consider σ1 horizontal, oriented at 45 degrees east of north and equal to twice the vertical stress, and σ2 horizontal, oriented at 135 degrees east of north and equal to 1.65 times the vertical stress. This orientation of σ1 is considered an ‘average’ value of measured orientation (which mostly varies between 0 and 90 degrees) and corresponds to the predominant trend in the The Journal of the Southern African Institute of Mining and Metallurgy

The role of rock mass heterogeneity and buckling mechanisms in excavation performance database (Blake, 2015). Recent field observations of seismicity around the stopes and drifts also agree with a major principal stress oriented NE-SW. The magnitude of the principal stress considers the high end of the measurements.

Buckling scheme Site observations It can be observed on site (as shown in Figure 4) that drifts trending parallel to foliation strike in Unit C (prevalent in the footwall) experience deep sidewall buckling, which worsens over time (IAMGOLD, 2017b). Intensity varies greatly, and the resulting convergence rate can be as high as 0.6 m in two weeks. The same pattern is always observed: the top of the south wall and bottom of the north wall buckle in. Indeed, Unit C is one of the most highly altered units, with weak foliation plane strength and tight foliation spacing. In consequence, significant rehabilitation is required in long-term haulage accesses driven parallel to foliation. The effective span of drifts grows much larger than the initial mined span due to the presence of low confinement ‘buckling corridors’ (Mercier-Langevin and Hadjigeorgiou, 2011; Karampinos, 2016), as shown in Figure 4.

At Westwood, the impact of buckling on the stability of openings trending in the E-W direction was significant in the area of case study 132-03. In this area, where mostly Unit C is present, haulage drifts are located in the hangingwall and parallel to foliation on multiple levels, with a 20 to 25 m offset from stoping activity. Significant closure of the haulage was experienced on level 132-03 and floor heave occurred on level 132-04, both attributed to buckling of the stope walls. In general, an improvement of access performance could be observed where the haulage drive deviated from the foliation direction. Buckling and consequent squeezing is also frequently observed at a smaller scale when excavating blast-holes and V-30s (i.e. raisebored slots) as shown in Figure 4. The impact of buckling on blast-hole performance has been studied in detail through case study 230.

Buckling scheme implementation Squeezing conditions in hard rock mines have been studied by various authors, and a summary of recent work (Potvin and Hadjigeorgiou, 2008; Mercier-Langevin and Wilson, 2013) is given by Karampinos (2016). In particular, about squeezing in foliated rock: ‘[…] the stress redistribution around an opening results in loading of the intact rock in a parallel direction to the foliation planes. This leads in contraction along the foliation and dilation towards the opening. This dilation increases the

deflection of the rock layers and decreases the critical buckling load. Bulking appears orthogonal to the foliation as the foliation planes open up. As buckling occurs in the sidewalls, this process is transferred deeper into the rock mass. The buckling process in the sidewalls results in an increased effective span, and reduces the confinement provided to the back and the floor as well as the friction between the foliation planes in these areas. At the early squeezing stages, there is a higher convergence rate in sidewalls whereas later in the process the closure rate reduces in the sidewalls and increases in the back.’

FLAC3D is not well suited to represent the buckling mechanism at Westwood because of its continuum formulation.

To enhance buckling representation, an ad-hoc scheme has been added to the CaveHoek constitutive model used for the simulation. Regularly along the analysis, the stress normal to the foliation and the major stress in the plane of the foliation are calculated: if for a given zone, the stress normal to the foliation is below a certain threshold, and if for this same zone the major compression stress in the foliation plane is above a certain threshold, this zone is considered to have buckled. If buckled, the zone’s stress tensor is reinitialized to zero and residual properties are applied to this zone. This scheme allows reproduction of the consequences of the buckling in terms of deconfinement and stress redistribution around an opening. However, buckling is not explicitly modelled, so the large magnitude of associated buckling deformations is not simulated accurately. The buckling scheme implementation has been done in two steps: 1. C alibrate buckling thresholds (normal and in-plane foliation stress) at the drift scale, looking at one drift (with comparison with the equivalent 3DEC model for which buckling is emergent) and multiple stacked drifts 2. Study and ensure that the scheme is able to reproduce the main aspects of case studies 104 (large effective span of excavations), 132 (impact of stoping on crosscuts and haulage accesses), and 230 (squeezing of blast-holes in primaries and not in secondaries) with limited calibration of parameters.

Single and stacked drift behaviour A drift-scale model (5 m span at 1200 m depth) was first implemented to test the model assumptions and buckling scheme, and ensure that drift behaviour was aligned with site observations. As can be seen in Figure 5, the buckling scheme is critical to achieve deep deconfinement of the drift walls as expected in a buckling-prone rock. Site observations also indicate

Figure 4—Left: Example of buckling in E-W drifts at Westwood, looking east (courtesy of the mine). Centre: Example of DEM model with emergent buckling. Right: Hole squeezing in a V-30 (courtesy of the mine) The Journal of the Southern African Institute of Mining and Metallurgy

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The role of rock mass heterogeneity and buckling mechanisms in excavation performance Back-analysis 104 area (simplified model)

Figure 5—Example of FLAC3D drift simulation performed without and with the buckling scheme. — Contours of σ1 (in MPa – negative values indicate compression)

Figure 6—Comparison of 3DEC and FLAC3D (with buckling scheme) single drift and stacked drifts simulations. Contour of stress (in MPa – negative values indicate compression)

that buckling could be observed about 4 m deep into the wall at crosscuts, which is aligned with the model deconfinement depth.

Also, comparison between the FLAC3D model and the 3DEC model (from which buckling is emergent) gives similar results in terms of stress redistribution, as shown in Figure 6.

When rock mass properties are varied from the base case assumptions, results in terms of stress redistribution are not significantly variable (i.e., deconfinement up to approx. one span on each side). This suggests a geometric effect of the joints, which is also observed in 3DEC. The following buckling stress thresholds, for a given zone, seem to give reasonable results: stress normal to the foliation below 2 MPa and for this same zone, compression stress in the foliation plane above 20 MPa. Increasing the normal stress threshold and decreasing the compression stress threshold makes the rock mass more susceptible to buckling (and hence deepens the deconfinement zone), but the model is not highly sensitive to these thresholds at the drift scale. After the drift-scale model was calibrated and gave reasonable results, a model of three stacked drifts (5 m span at 1200 m depth) was simulated. The distance between levels was about 15 m, which is similar to the ‘Coke Can’ configuration in the 104 area where significant stability issues have been encountered. Comparison between the FLAC3D and the 3DEC models (from which buckling is emergent) gave similar results (see Figure 6) in terms of stress redistribution and, as expected, due to the large effective span, stress is concentrated between levels (σ1 > 120 MPa). The deconfinement in the roof and floor of the 3DEC model of the stacked drifts seems unrealistic and is due to properties of the matrix used in the 3DEC model (likely too weak). Properties were readjusted for the single drift model, but this stacked drift model was not rerun due to time constraints.

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After the drift-scale models were studied, a simple model of the 104 area was simulated. This model is in the vicinity of the socalled ‘Coke Can’, which is a cylindrical volume of ground located in a section of the ramp and adjacent to the N-S access of the 104 area between levels 104-06 and 104-10. In this area, the extraction ratio was high due to the tight level spacing (< 15 m) and extensive excavation of sub-levels perpendicular to the main N-S ramp accesses (Figure 7). This model aimed to reproduce the buckling in the walls of the accesses that led to the increase of the effective span and the yielding of the core of the accesses on levels 104-06 to 104-10. The subsequent seismic events and further yielding (IAMGOLD, 2015) were not studied as part of the calibration process presented in this paper. Figure 7 shows the geometry of the model and zones that have buckled for one of the tested cases. As expected, excavations oriented E-W exhibit buckling of the walls because the foliation is dipping south.

South of the ramp, where E-W sub-drifts are closely spaced, the buckled volumes tend to connect and generate a large overall effective span, much larger than the original span of the N-S access. This behaviour is critical to explain the yielding of this volume of ground at Westwood which led to large seismic events.

Case study 132-03 Case study 132-03 focused on an area of block 226A where significant convergence was observed in the crosscuts and haulage access on multiple levels adjacent to stoping. This level of damage was surprising given the standoff distance from stoping (30 m on 132-02, 25 to 30 m on 132-03). This area is in Unit C only (which is prone to buckling). Figure 8 shows the results of damage mapping in the area of interest on level 132-03. The damage was induced by the mining of two stopes between levels 132-02 and 132-03. Figure 8 also shows the geometry of the area of interest modelled in FLAC3D. Figure 9 shows σ1 and σ3 after excavation on transverse sections through the two stopes. Because of the deep buckling and associated deconfinement occurring in the walls of the stopes, the crosscut on level 132-03 is completely yielded and the volume of rock between levels 132-04 and 132-03 is experiencing elevated σ1 (70 to 100 MPa) and low confinement (< 25 MPa). This can explain why the haulage on level 132-04 was impacted by stoping and experienced floor heave.

Figure 10 shows a transverse section close to the roof of the haulage of level 132-02. It can be seen that the damage (lefthand plot) at the top of the south wall in the E-W drift is almost

Figure 7—Buckling around the Coke Can in the 104 area (BI = 85, SI = 95) The Journal of the Southern African Institute of Mining and Metallurgy

The role of rock mass heterogeneity and buckling mechanisms in excavation performance 4. T he negative impacts of stoping on the drifts in terms of stress concentration and deconfinement, especially on the 132-03 level.

Case study 230

Figure 8—Mapping damage of levels 132-03 along with area of interest of case study 132-03 (S0 = no visible damage, S1 = mild damage in the roof, S2 = moderate damage, S3 = high damage, S4 = heavy damage, S5 = extreme damage). Right: Geometry of the FLAC3D model used to analyse the 132-03 case study

Figure 9—Transverse section south-north of the contours of σ1 (top) and σ3 (bottom) around stopes 1 and 2 — FLAC3D analysis of case study 132-03 (SI = 100, BI = 80)

Case study 230 focuses on the sequence Z230C, where high stress conditions were observed. This case study was chosen for two specific behaviours: (1) significant blast-hole squeezing in primaries (purple stopes in Figure 11) but not in secondaries (yellow stopes in Figure 11), and (2) high level of microseismicity in the hangingwall of the upper level when mining the secondaries.

Only three stopes of the Z230C sequence were modelled as part of the case study: two primaries and one secondary (indicated by arrows in Figure 11). The hole squeezing aspect of this case study was studied by explicitly modelling the stopes and V-30s and excavating them in sequence. Figure 11 shows the stress tensors at the location of the V-30s, just prior to their excavation, for each of the stopes modelled (two primaries and one secondary). The stress tensors are coloured based on the magnitude of the E-W stress, the horizontal stress that is parallel to the foliation and is responsible for driving buckling. The model successfully demonstrated significantly more buckling in primaries than secondaries. Indeed, it can be seen that the E-W stress is quite high in primaries and leads to moderate to severe buckling, while the E-W stress is very low in secondaries (since it is shielded by primaries located on the east and west sides of the secondary) and hence no significant buckling is observed.

These observations indicate that high E-W (foliation-parallel)

Figure 10—Effect of drift orientation on damage and buckling in the wall, plan view at the top of level 132-02 — FLAC3D analysis of case study 132-03 (SI = 100, BI = 80)

absent in the section of the drift oriented NE-SW. This is due to the absence of buckling (right-hand plot) in NE-SW drifts for which the stress on the foliation is not high enough to induce significant buckling in the walls. These results are in accordance with observation throughout the mine that drifts oriented at least 30 degrees from the E-W direction tend to perform much better than E-W drifts, as shown by the damage mapping in Figure 8. All these results have been obtained with the base case properties, a strength index SI = 100, and a brittleness index BI = 80. Hence, by using directly the point load data to derive the strength of the rock mass through UJRM testing (without further degradation) and the buckling scheme, the following aspects of the 132-03 case study could be captured: 1. The stope wall instability due to deep buckling 2. The depth and severity of drift closure and buckling depth in E-W drift (pre-stoping) 3. The beneficial effects of re-orienting drifts from the E-W direction The Journal of the Southern African Institute of Mining and Metallurgy

Figure 11—Case study 230. Top: Differential behaviour of V-30s (760 mm diameter relief raises) between primaries and secondary. Middle: Stress tensor coloured by E-W stress magnitude and associated buckling in V-30s, before and after excavation of the V-30s, in primaries and secondary as modelled by FLAC3D (SI = 100, BI = 80). Bottom: V-30s convergence for different stress conditions from 3DEC model (buckling is emergent) VOLUME 120

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The role of rock mass heterogeneity and buckling mechanisms in excavation performance stress leads to blast-hole closure (in primaries), and not high induced N-S pillar stresses (in secondaries). This explains why secondaries (shielded from E-W stresses by primaries) experience less hole squeezing than primaries. Also, approximate threshold levels of E-W stress (prior to drilling) for hole squeezing were defined from the FLAC3D model: mild squeezing is expected for an E-W stress of 20 to 30 MPa, moderate squeezing for an E-W stress of 30-45 MPa, and severe squeezing is expected for an E-W stress above 45 MPa. These thresholds were validated by the 3DEC simulations of the excavation of V-30s under different stress conditions; these results are shown in Figure 11 as well.

The seismic aspect of this case study was unsuccessful; the location of the ‘seismic-prone’ rock in the model (hangingwall and footwall at stope level) based on stress evaluation did not match the location of seismicity (hangingwall of the second level). This may be due to an inaccurate representation of the local units or local properties of the units that localized the seismicity into specific areas.

Conclusion

Blake, W., Kaiser, P.K., and Simser, B. 2015. Rockburst Review Committee Report, Supplementary Technical Report, Submitted October 5 2015. Westwood Mine, IAMGOLD. Board, M. and Pierce, M. 2009. A review of recent experience in modelling of caving. Proceedings of the International Workshop on Numerical Modeling for Underground Mine Excavation Design and 43rd US Rock Mechanics Symposium. NIOSH Pittsburgh Research Laboratory, Pittsburgh, PA. Clark, I.H. 2006. Simulation of rockmass strength using ubiquitous joints.

Proceedings of the 4th International Symposium on Numerical Modeling in Geomechanics, Madrid, Spain, 29-31 May. Hart R. and Varona, P. (eds). Itasca Consulting Group, Minneapolis, MN. Paper no. 08-07.

Hoek, E., Carranza-Torres, C., and Corkum, B. 2002 Hoek-Brown failure criterion - 2002 edition. NARMS-TAC 2002: Mining and Tunnelling Innovation and Opportunity, vol. 1, pp. 267–273. Hammah, R., Bawden, W., Curran, J., and Telesnicki, M. (eds). University of Toronto Press.

IAMGOLD. 2017a. Mineralization and geology of the Westwood deposit. PowerPoint presentation for internal communication, 56 slides. January 2017.

IAMGOLD. 2017b. Westwood background presentation. PowerPoint presentation for

We are able to simulate rock mass performance at Westwood only if all of the following aspects are included in the model: 1. Use of PLT and laboratory data as input to UJRM tests to estimate excavation-scale anisotropic rock mass strength, with minimal additional downgrading in the subsequent calibration process (equivalent to SI 98 to 100) 2. Brittle rock mass matrix and foliation response 3. Incorporation of a scheme to account for the deconfinement that accompanies buckling around excavations (buckling represented implicitly within the model with destressing and weakening driven by high foliation-parallel stress coupled with low normal stress).

The incorporation of these aspects of behaviour into the rock mass numerical behaviour has allowed for a successful match to rock mass performance in case studies 132-03 and 230, with demonstrated ability to capture drift closure, hole squeezing, stope wall instability, and the impact of the drift’s orientation; and a successful simulation of large-scale ground yielding in the presence of tightly spaced accesses under high local extraction ratio in the 104 area (the ‘Coke Can’), with buckling being the essential mechanism to increase the effective span of the NS accesses and push stresses out to the rock mass beside and below. To refine the rock mass model behaviour for future work, the buckling scheme could be refined by making destressing dependent on the stress level instead of zeroing the stress in the buckled zone, and the rock strength distribution could be explicitly embedded in the large-scale simulations, similarly to what was done for the UCS tests of the UJRM.

Acknowledgement

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internal communication, 101 slides. January 2017.

IAMGOLD. 2015. Analysis of May 26th seismic events and general seismicity at Westwood mine. – PowerPoint presentation, peer review 25-26 September 2015, 162 slides. September 2015.

Itasca Consulting Group Inc. 2017. FLAC3D — fast Lagrangian analysis of continua in 3 dimensions, version 6.0. Minneapolis, MN. Itasca Consulting Group Inc. 2016. 3DEC — three-dimensional distinct element code, version 5.2. Minneapolis, MN. ISRM. 1984. Suggested method for determining point load strength. ISRM: Point load test, RTH 325-89, 10 June 1984. http://www.geoplanning.it/test/ wp-content/uploads/2012/02/Suggest-method-for-determining-Point-LoadStrength.pdf Kalenchuk, K.S., Mercer, R., and Williams, E. 2017. Large-magnitude seismicity at the Westwood mine, Quebec, Canada. Proceedings of the Eighth International Conference on Deep and High Stress Mining. Wesseloo J. (ed.). Australian Centre for Geomechanics, Perth. pp. 89–101.

Karampinos, E. 2016. Management of squeezing ground conditions in hard rock mines. PhD thesis, Department of Civil Engineering, University of Toronto.

Lorig, L. and Pierce, M. 2000. Methodology and guidelines for numerical modelling of undercut and extraction level behaviour in caving mines. Report to the International Caving Study. Itasca Consulting Group, Minneapolis, MN. Mercier-Langevin, F. and Hadjigeorgiou, J. 2011. Towards a better understanding of squeezing potential in hard rock mines. Mining Technology, vol. 120, no. 1. pp. 36–44.

Mercier-Langevin, F. and Wilson, D. 2013. Lapa Mine – ground control practices in extreme squeezing ground. Proceedings of the Seventh International Symposium on Ground Support in Mining and Underground Construction.

Potvin Y. and Brady B. (eds). Australian Centre for Geomechanics, Perth. pp. 119–131.

Pierce, M. 2013. Numerical modeling of rock mass weakening, bulking and

Itasca acknowledges and thanks IAMGOLD and the Westwood geotechnical team (Emilie Williams, Patrick Ferland, Hugo Fisette, Maxim Martel, Jean-Francois Dupuis, and Daniel Vallières) for providing insights into the mine’s ground behaviour in addition to qualitative input data, as well the authorization to publish this work. Itasca also acknowledges Patrick Andrieux for facilitating our participation in this project. Thanks to Rob Mercer and the external review board (John Hadjigeorgiou, Peter Kaiser, and Brad Simser) which helped guide this work and brought useful ideas and comments. The authors also wish to acknowledge Thierry Lavoie for contributing to aspects of this work.

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softening associated with cave mining. ARMA e-newsletter. ARMA Publications Committee, Spring 2013.

Potvin, Y. and Hadjigeorgiou, J. 2008. Ground support strategies to control large deformations in mining excavations. Journal of the Southern African Institute of Mining and Metallurgy, vol. 108, no. 7. pp. 397–404.

Sainsbury, B-A, Pierce, M, and Ivars, D.M. 2008. Simulation of rock mass strength anisotropy and scale effects using a ubiquitous joint rock mass (UJRM) model.

Proceedings of the First International FLAC/DEM Symposium on Numerical Modeling, (Continuum and Distinct Element Numerical Modeling in GeoEngineering). Hart, R., Detournay, C., and Cundall, P. (ed.). Itasca Consulting Group, Minneapolis, MN. Paper 06-02.

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The Journal of the Southern African Institute of Mining and Metallurgy

Applications for the Hovermap autonomous drone system in underground mining operations E. Jones1, J. Sofonia2, C. Canales3, S. Hrabar2, and F. Kendoul2 Affiliation: 1 BHP, Adelaide, SA, Australia.

2 Emesent, Brisbane, QLD, Australia.

3 Universidad de Concepción, Chile.

Correspondence to: E. Jones

Email: evan.jones1@bhp.com

Synopsis The development and application of the Hovermap autonomous flight system are discussed in relation to underground mining, with examples from its early adoption. The current performance of the system and subsequent data interpretation suggest some scenarios in which Hovermap deployment is appropriate.

The examples discussed focus principally on improving the detail of observational data from inaccessible areas commonly encountered in underground mines. These insights can then be used in design review and management processes. Recent and future developments in the hardware, software platforms, and the associated data analytics are outlined.

Keywords LiDAR; automation; drone; underground mining.

Dates:

Received: 1 Aug. 2019 Revised:5 Dec. 2019 Accepted: 22 Jan. 2020 Published: January 2020

How to cite:

Jones, E., Sofonia, J., Canales, C., Hrabar, S., and Kendoul, F. Applications for the Hovermap autonomous drone system in underground mining operations.

The Southern African Insitute of Mining and Metallurgy DOI ID: http://dx.doi.org/10.17159/24119717/862/2020

This paper was first presented at the Deep Mining 2019 Conference, 24–25 June 2019 Misty Hills Conference Centre, Muldersdrift, Johannesburg, South Africa.

Introduction Mining in deep and high-stress conditions inherently involves risks to both personnel safety and the mining operation. Operating in extreme conditions requires an understanding of the rock mass response to mining-induced stress changes. Mobile scanning data-acquisition methods and interpretations in underground mines have emerged since 2014 (Mining Magazine, 2014). They have been shown to improve the safety, design, and decision-making processes by mining and geotechnical engineers (Beck and Campbell, 2019). More recently, semi- and fully-autonomous drones have facilitated data acquisition for geotechnical rock mass observation and analysis. Just as handheld mobile scanning platforms have provided data within human-accessible areas, drones are now acquiring data from otherwise inaccessible areas.

Overview of the study development of hovermap Underground mining presents many accessibility challenges. For mines with added challenges such as seismicity, the management of these hazards further increases areas of exclusion. The willingness of companies to permit workers’ exposure to such hazards has decreased over recent decades, yet has decreased over recent decades focus on safety has often been associated with increases in the labour, time and cost required to undertake additional inspections. The development of an autonomous dronebased mobile mapping platform provides a viable solution by enabling the remote acquisition of data from excluded and hazardous areas, allowing inspection and analysis without compromising the safety of personnel. The solution presented is a payload that allows commercially available drones to fly in GPS-denied environments.

Over the past two decades, the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) has been at the forefront of research into industrial robotics and the development of 3D simultaneous localization and mapping (SLAM) algorithms (Zlot and Bosse, 2012). A specialized group within CSIRO was formed to combine these two internal specialties, enabling the autonomous flight of drones in GPS-denied environments. The SLAM-based autonomous drones were the original prototypes for what is now known as Hovermap. Emesent, a spin-out of CSIRO, has commercialized autonomous drone technology, utilizing real-time processing of 3D SLAM algorithms and further advanced the drone autonomy technologies. The real-time point cloud solution used for drone autonomy can be reprocessed to create a high-resolution point cloud of the environment along the flight path for the purpose of analytics and inspections.

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Applications for the Hovermap autonomous drone system in underground mining operations Two LiDAR orientations were developed for the trials and made available commercially. Initially, the LiDAR sensor was located beneath the housing such that is was vertically oriented (Figure 1A). The results showed a region of low point density above the drone due to the shadowing of the aircraft platform.

This shadowing coincides with areas that did not have good scan coverage and as such, proved problematic for applications such as drawpoint hangups and coverage of stope crowns. Subsequently, a forward-facing, horizontal orientation was developed (Figure 1B), relocating the shadow to behind the drone in areas previously scanned and with good coverage. This horizontal orientation, therefore, demonstrated as the optimal configuration for underground mining applications.

Design and engineering of an autonomous drone system Hovermap is a mobile LiDAR scanner system that can be mounted as a payload beneath a drone, enabling autonomy in GPS-denied environments. It is comprised of a multi-channel LiDAR, an inertial measurement unit (IMU), and an onboard computer. The LiDAR sensor mechanically rotates, changing the intended output from a 360° planar field of view into a 360° × 360° spherical field of view. For position recognition and sensing, the LiDAR is coupled with a low-grade microelectrical-mechanical system (MEMS) IMU. The IMU data and LiDAR results are processed onboard in real time within the SLAM algorithms to generate the point cloud of the scanned environment. This SLAM solution can interface with the flight controller of compatible drones to provide omnidirectional collision avoidance, position hold, and programmed flights in the GPS-denied and dark environments associated with underground mining operations.

Table I summarizes the capabilities of the Hovermap system.

Methods for obtaining results

The process of obtaining point cloud data with Hovermap for further analysis can be summarized by four steps, outlined in Figure 2.

Table I

Capabilities of the Hovermap system Description Comment Hovermap Sensor LiDAR

Velodyne VLP-16 Lite (Puck)

LiDAR rotates to produce near 360˚ x 360˚ field of view

– Up to 100 m range

Operation range is typically 60–80 m

– 16 channels

Returns depend on environment

– Up to 300 000 points/s

Useful for complex environments.

– Dual return Drone Type Flight time IP rating

DJI M210 or M600

Depends on flight type and required duration

10–30 min

Dependent on drone and flying environment

Variable: none - IP57

Drone-dependent.

Mapping and Results

SLAM

Point cloud processing time

Point cloud output format

Local solution: Real-time Global solution: post-processed

Used for collision avoidance and position hold Full point cloud solution for analytics.

Local: Real-time during flight Global: 2 x flight time

Displayed to pilot Full point cloud solution for analytics.

.laz and .ply

Each point contains the following attributes

and attributes

– Location (x, y, z, )

– Range

Accuracy* ± 30 mm

Precision*

– Intensity

– LiDAR channel number – Time since scan strart.

Raw data (limited by sensor output at a distance of 100 m). methods and limited range in underground environments.

~±10 mm

Repeatability for local point cloud alignment.

* For a detailed discussion on these topics for underground mobile mapping refer to Jones, Ghabraie, and Beck, (2018)

Figure 1—(A) The vertically oriented Hovermap used for the initial underground mining trials. (B) The horizontally oriented Hovermap preferred for use in underground mines, providing greater coverage above the drone

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Applications for the Hovermap autonomous drone system in underground mining operations

Figure 2—An overview of the steps required for obtaining data from Hovermap

Hovermap provides various levels of automation to a drone. In this study, three methods of flight automation were demonstrated in mining environments: ➤ Autonomy Level 1 – Pilot-assisted mode

– Omnidirectional collision avoidance (virtual bubble surrounding the drone)

– GPS-denied position hold and flight

– Enables safe flight close to structures in GPS-denied environments ➤ Autonomy Level 2a – Waypoint mode

– Set waypoints in an existing map for routine preprogrammed GPS-denied flight

– Enables beyond-line-of-sight flight with almost no input from pilot ➤ Autonomy Level 2b – Tap-to-fly interactive waypoint mode in live stream map

– Set waypoints in a map streamed directly from Hovermap during a flight

– Includes collision avoidance

– Enables beyond-line-of-sight flight with minimal input form pilot.

The required duration of a scan is governed by the mission requirements, including factors such as:

➤ The area of data capture ➤ Required point cloud density ➤ Complexity of features within the scan area ➤ The battery life of the drone ➤ Access restrictions.

Flight speeds in underground environments vary depending on the purpose of the flight, air flow speed, and complexity of the environment. Typical underground flight speeds along drives and crosscuts are currently 1-2 m/s. The sensor has also been mounted to a light vehicle and driven at speeds up to 5.5 m/s.

For applications such as flying within a narrow-vein or sublevel open stope, the required flight duration is approximately 5 minutes. This depends on the size of the stope, distance to the stope access, and the required point density within the stope. Although the two stoping methods vary, the flight time is consistent due to flight speeds and proximity to the stope walls.

In both cases the resulting point cloud has a higher point density and coverage than conventional stope scanning methods.

Onboard data storage is 480 GB, while the data acquisition rate is approximately 300 MB/min, allowing for full day of data capture. Following a scan, data is transferred from the onboard computer to a secondary processing computer for computation of the global SLAM solution.

SLAM processing is conducted in two stages. The first stage occurs during the scan, providing a fixed time-period local solution. This local solution provides the relative placement of each new datum relative to prior known data-points and features. It is this local ‘image’ of the current environment that, The Journal of the Southern African Institute of Mining and Metallurgy

when coupled with a drone flight controller, provides collision avoidance, position hold, and allows for the placement of waypoints to aid navigation in the GPS-denied environment. The second stage of SLAM processing is conducted after scanning, on a separate computer, providing the global SLAM solution and final point cloud. This global solution uses a range of data from the local SLAM solution, IMU, flight path, and feature recognition throughout the entire scan to produce a correctly scaled and locally referenced solution. If multiple flights have a region of overlap, the processing software accurately merges the multiple scans during processing of the global SLAM solution.

The final output from the post-processing is a high-resolution point cloud containing each of the returns, as well as the trajectory of the scanner. The data format is exported as a nonproprietary, open source format (eg. .laz, .ply, .dxf). Each data-set contains multiple ‘attributes’ as outlined in Table I. Each attribute can be analysed and appropriately filtered to ensure that only the most accurate data-sets are used in further analysis. As the data is in standard point cloud format, subsequent analysis can be conducted using many commercially available or open-source software packages.

Georeferencing point cloud results The registration of point clouds into the mine’s local coordinate system can benefit the interpretation for many mining applications. This allows scans to be interpreted with additional local information such a lithology, structure, void, or numerical modelling parameters such as stress. There are multiple methods of georeferencing 3D point clouds, including the use of survey spheres and iterative closest point (ICP) methods.

Survey spheres Survey spheres have been used successfully for the purpose of georeferencing a point cloud to a mine’s coordinates, yet the approach is more time-consuming than ICP methods. The method involves the placement of three or more survey prisms on the wall (Figure 3). The coordinates of the survey sphere are surveyed using a total station or similar device. During the 3D scan additional time is spent scanning the survey sphere, creating a high-density point cloud and allowing for easy recognition during the post-processing alignment. For the post-processing alignment, a spherical object of the same size as the survey target is created and located at the surveyed location. The high-density spherical point cloud is then fitted to the corresponding spherical object (Figure 4).

The final accuracy of the georeferenced 3D scan relies on ensuring the fitted spherical object is located at the surveyed location, as well as the accuracy of the 3D scan alignment to the spherical object. The alignment of the high-density point cloud to the spherical object can prove difficult if the scan contains a high level of noise, as shown in Figure 4. In this instance, it was found that the high reflectivity of the survey sphere caused the VOLUME 120

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Figure 3—(A) A survey sphere is placed on the wall. (B) Either the collar or the sphere centroid location is known, so the point cloud can be referenced to these locations

Figure 4—(A) The results from a Hovermap point cloud and the alignment of a survey sphere. (B) Highly reflective spheres can produce noisy data, reducing the accuracy of the fitted sphere. The georeferencing of noisy data requires careful processing

LiDAR sensor to saturate and scatter the resulting data-points.

Under these circumstances, the placement of four or more spheres throughout the scan can constrain the degrees of freedom of the global alignment, and with careful processing can improve the accuracy of the point cloud registration. Although georeferencing using survey spheres is a manual alignment process, the advantage is that the survey spheres are at known locations within the mine’s coordinate system and the global accuracy of the 3D scan can be calculated.

Iterative closest point registration (point cloud to point cloud or point cloud to mesh) ICP registration is an iterative method for aligning a point cloud to either a known solid shape, such as a 3D mesh, or to another point cloud. The algorithm is essentially a 3D residual mean square method in the sense that a predetermined allowable error is set and the algorithm will continue to reduce the residual error until either the threshold is met or a maximum number of iterations have been conducted. This method works very well in an underground mining environment as each excavation has a number of features and adjoining intersections. These intersections constrain the final alignment of point cloud registration, improving the accuracy of the final alignment to mine coordinates.

This method works particularly well with Hovermap data, as the LiDAR sensor and SLAM algorithms currently used have proven to produce point clouds with far fewer drift errors than the earlier mobile LiDAR systems used by Jones, Ghabraie, and Beck (2018). This means the use of ICP alignment methods can align Hovermap results into the mine grid very rapidly when another known shape, or pre-existing point cloud, is available.

Summary of georeferencing There are applications where georeferencing 3D point cloud data into the mine’s coordinate system can improve the interpretation

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of the data. Two methods for georeferencing have been presented, both of which require manual steps within the alignment process.

The accuracy of the final georeferenced point cloud can be affected by factors such as the reflectivity of the surface material and hardware errors. The best method to use depends partly on the available information and on the required accuracy of the final alignment.

Case studies for improving mining safety, productivity, and efficiency Increasing workplace safety is a requirement in underground mines globally. Elimination of people from hazardous environments is one of the benefits of remotely operated autonomous systems. Furthermore, the additional coverage within hazardous environments allows for improved and more in-depth analytics for decision-making. Underground case studies are presented in the following sections, providing an overview of applications where Hovermap data is improving safety and mining insights.

Stope analysis Initial applications of Hovermap have focused on stope inspections and analysis, the benefits being from a safety and efficiency perspective during data capture, and an analytics and operational perspective from the results.

During data capture in a stope, safety is improved by reducing the exposure (in terms of both proximity and duration) of the surveyors undertaking the inspection near the stope’s edge or brow. Current cavity monitoring systems (CMS) require surveyors to access the stope as close as possible to the drawpoint or edge of the stope so that the CMS scanner can be positioned as far into the stope as possible. At the drawpoints this can expose the surveyors to sudden rockfall hazards within the stope. Access to the upper levels of an open stope can expose them to any unknown undercutting if it has occurred. At the completion of a The Journal of the Southern African Institute of Mining and Metallurgy

Applications for the Hovermap autonomous drone system in underground mining operations CMS scan the surveyors must then retrieve the device, exposing them a second time. A major advantage of the drone from a risk perspective is that it can be operated from a safe distance from the stope, completely removing the risk involved in current CMS methods. The efficiency of data capture is improved as the duration to set up, fly into the stope, and return is typically less than 10 minutes. The results can then be used for an in-depth review of the stope conditions.

Improved data analytics is possible as the drone can fly beyond visual line of sight (BVLOS), past the brow of the stope or hung-up drawpoint. This unprecedented access provides a detailed view of brow conditions and other occluded areas within the stope. The mobile scan significantly reduces shadowing within the results compared to conventional scanning methods, in that current CMS methods use a rotating laser positioned at a static location within a stope. The distribution of measurements is such that there is a high density of data near to the scanner, diminishing with distance. The spatial resolution of CMS data varies from thousands of points to single points per square metre.

In comparison, Hovermap data is consistently in this upper range

throughout the regions of the stope. A comparison between current CMS and Hovermap results for the same stope is shown in Figure 5.

The high-resolution point cloud produced by Hovermap can provide analytics such as stope volume to be reconciled with greater confidence for an indication of production tons and required backfill (where applicable). The precision and density of the Hovermap point clouds allow for the recognition of structural traces and planes (Figure 6), providing geotechnical engineers with insight into the mechanisms responsible for over- and underbreak. The structural characteristics such as dip and azimuth, persistence, roughness, and spacing of features can be extracted and used for rock mass characterization and design purposes. Figure 7 shows an example where over 80 individual geological structures were identified.

Larger scale structures identified from the high-resolution point clouds can provide geologists and geotechnical engineers with insights for defining the structural model of the mine (Figure 8). These structures also aid in the rock mass classification and domaining parameters used for excavation stability assessments

Figure 5—Comparison of scans from a traditional CMS (left) and Hovermap (right) showing the detail of a final stope shape

Figure 6—The point cloud density from Hovermap provides sufficient detail for the recognition of structural traces (A) and structural planes (B) The Journal of the Southern African Institute of Mining and Metallurgy

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Figure 7—(A) A stope hangingwall showing the resolution of data achieved with Hovermap. (B) The planar facets recognized from commercially available software (Sirovision)

Figure 8—Large-scale geological structures identified from Hovermap data within a stope

for both development and stopes. Meanwhile, mining engineers can adjust stope dimensions and blast patterns to accommodate the increased knowledge of and confidence in the structures and rock mass characteristics.

The rapid acquisition of detailed point clouds from within stopes is assisting the trial sites and sites that have adopted the technology with improved scheduling and control on stopes. Sites are able to more regularly monitor stope volumes, providing greater insights into the remaining production stocks, fragmentation, and the recognition of instabilities such as chimneying or caving.

Backfill Monitoring The use of backfill in stopes is largely attributed to the requirement for achieving regional ground control within a mining area. The mode of in-stope support is related to a combination of the rock mass characteristics, the mode of deformation, and the engineered material properties of the fill, as discussed by Brady and Brown (2004, p. 408). In many highstress mining environments where a high extraction ratio is planned, backfill is used a support element to control unravelling and consequent dilution, and to assist in global stability, acting similarly to a pillar controlling hangingwall and footwall closure.

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In cases where backfill is used for engineering purposes, the monitoring of the material in high detail can be used to ensure the correct design is being followed. Where a higher strength plug is formed, the accurate measurement of the fill can be used to confirm the correct placement of the material in all areas of the stope. This same confirmation of design can be applied to sites where it is planned to develop back through a backfilled stope, reducing the risk of breaking through to a lower strength material. An example of a stope in the process of backfilling is shown in Figure 9.

In cases where the backfill is not used as an engineered material, regular inspections of backfill height can show the remaining volume available for waste. This knowledge can directly feed into the scheduling requirements for the mine.

Hangup inspections Drawpoint hangups in both stoping mines and caving mines pose a safety hazard to those attempting to clear them and come at a great cost in lost production. Current methods for clearing include blasting and hosing in an attempt to generate movement around the blockage, eventually dislodging the material blocking the drawpoint. Using Hovermap results, mining professionals can capture a better perspective of the blockage for a more targeted The Journal of the Southern African Institute of Mining and Metallurgy

Applications for the Hovermap autonomous drone system in underground mining operations

Figure 9—A data-set showing backfill height with the red line indicating the drone’s flight path

Figure 10—An example of a scan showing a drawpoint hangup

approach to its removal. The example shown in Figure 10 is from a block cave where the drone pilot was located in the extraction drive, away from the drawpoint and safely removed from any potential movement. In this example, the Hovermap payload was in the vertical-facing alignment, hence areas directly above the drone were occluded. This highlights the importance of selecting the correct sensor orientation for the task. A horizontal, forwardfacing orientation would have achieved a higher point density in the backs, delivering a clearer image of the hangup.

Deformation monitoring and ground support analysis Most rock mass hazards that personnel are exposed to in underground mines are within development drives. Mining professionals place great importance on ground support for providing a safe working environment, particularly in deep and high-stress mines that are typically subject to dynamic deformation. Jones and Beck (2017) describe mobile scanning hardware and methods for deformation monitoring in underground mines. The accuracy of mobile scanning devices such as Hovermap is sufficient for the recognition of convergence trends and for indicating areas of change in the backs and sidewalls greater than 10 mm. For some stiff rock masses that exhibit rapid, brittle failure, these trends may not be observed using this method. However, in most cases where mobile scanning has been implemented, the change detection has provided greater insights than broad-scale observational The Journal of the Southern African Institute of Mining and Metallurgy

mapping, and greater coverage than the sub-millimetre extensometer monitoring the sites had previously conducted.

The results from mobile LiDAR provide a range of benefits for deformation monitoring. The trends created from multiple scans indicate closure rates, which in turn provide guidance for the residual capacity, and scheduling for rehabilitation.

The displacements provide quantitative data for administrative controls such as Trigger, Action, Response Plans (TARPs).

With the inclusion of intensity as a point cloud parameter, the distinction between rock and metal is evident (Figure 11), allowing for improved methods of QA/QC and insights into whether the ground support is acting as a system or as individual elements. New and exciting analytical methods are being developed to better understand rock mass deformation now that near-spatially-continuous, accurate data-sets are collected minewide with mobile scanning systems.

Summary of case studies The applications for the data collected by Hovermap are extensive, improving safety, efficiency, and productivity in underground mines. The case studies outlined above demonstrate just some of the many ways the data may be used by mining and geotechnical engineers, surveyors, and geologists. The enhanced data-sets obtained from the use of Hovermap in underground operations is leading to ongoing research, allowing for the continuous advancement of applications in mining.

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Figure 11—An example of clarity of results collected from a single scan of a drive. Colour denotes the intensity of the returning waveform, highlighting the location of metal bolt plates

The future of automated drones in underground mining Research to further improve the capabilities of autonomous drones continues. Research outcomes that will soon be deployed include: ➤ Autonomy Level 3 – Autonomous exploration

– Single-click mission execution

– No waypoints, just point – click – explore – return ➤ SLAM-based auto-registration

– SLAM-based auto-registration utilizes the metadata collected by hardware to join multiple scans and create a single result, as though it were a single scan

– Quick turnaround on results requiring change detection ➤ Colourization of point clouds

– Provides additional data for classification

– Photographic-like imagery for further interpretation ➤ Additional sensors

– Gas detection

– Thermal imaging cameras

– Hyperspectral cameras ➤ Remote deployment

– Search-and- rescue missions

– Remote re-entry following blasting or seismic events.

The analytics derived from these hardware improvements will result in greater insights into many aspects of a mining operation. However, this requires the development of both IT infrastructure and software capable of storing, analysing, and visualizing the detailed data-sets.

The application of drones and mobile sensor technology in underground mines is in its infancy. During the two years of trials of the Hovermap system and 12 months since commercialization, the number of applications and sites adopting the technology have steadily increased.

The adoption of mobile mapping techniques has been increasing since the commercial release of the Zeb-1 mobile handheld LiDAR scanner in 2014. The results from this method of data acquisition have shown to provide valuable insights for mining engineering design. Systems previously available either required to be operated by hand or vehicle mounted. These methods have not allowed for any additional information to be acquired in access-restricted environments.

Hovermap has been developed as a drone-mounted payload, providing collision avoidance and drone autonomy, while the resulting 3D point clouds can be georeferenced for analytics.

This system addresses the issue of remote access into areas that JANUARY 2020

Acknowledgements Emesent is grateful to Barrick and Northern Star Resources for their continual assistance during the site trials period.

References Beck, D. and Campbell, A. 2019. Operations management in seismically active mines.

Proceedings of the 53rd US Rock Mechanics/Geomechanics Symposium, New York, 23-26 June. American Rock Mechanics Association, Alexandria, VA. Brady, B. and Brown, E. 2004. Rock Mechanics for Underground Mining (3rd edn).

Kluwer Academic Publishers, Dordrecht, The Netherlands.

Discussion and conclusion

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were previously inaccessible or were deemed too hazardous for mining personnel to enter. The unique combination of autonomy, advanced data acquisition, and high-resolution 3D mobile mapping is providing new insights into underground mine engineering, thus improving safety and mine design.

The site trials mentioned in this article, and commercial deployment since, have produced results that have directly improved safety, efficiency, and productivity in the adopting mines. The examples provided above are some of the more obvious examples of the applications of the data collected.

Research and development into additional applications and the automation of processes are ongoing and constitute a major part of the future development of Hovermap and the data it produces.

As drone technology improves, longer flights, and thus scanning times, will be possible. The hardware will be reduced in size, allowing access into tighter spaces, and will become more robust, allowing operation in difficult environments.

Advances in sensor technology will generate more accurate and precise point clouds, with longer ranges and a higher resolution.

Streamlining the data analytics will get results into the hands of engineers faster, speeding up the decision-making processes. In the end, these outcomes will assist mining companies to achieve their aim of removing personnel from hazardous underground environments, while improving the knowledge of the rock mass and its behaviour.

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Jones, E. and Beck, D. 2017. The use of three-dimensional laser scanning for deformation monitoring in underground mines. Proceedings of the 13th AusIMM Underground Operators Conference, Gold Coast, Australia.

Australasian Institute of Mining and Metallurgy, Melbourne. pp. 267–270.

Jones, E., Ghabraie, B., and Beck, D. 2018. A method for determining the field accuracy of mobile laser scanning devices for geomechanics. Proceedings of the 10th Asian Rock Mechanics Symposium, Singapore, 29 October–3 November.

International Society for Rock Mechanics and Rock Engineering, Lisbon.

Mining Magazine. 2014. Laser scanner put through paces in SA. https://www. miningmagazine.com/equipment/news/1258218/laser-scanner-paces-sa [accessed 16 December 2019].

Zlot, R. and Bosse, M. 2012. Efficient large-scale 3D mobile mapping and surface reconstruction of an underground mine. Journal of Field Robotics, vol. 31, no. 5. pp. 758–779.

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The Journal of the Southern African Institute of Mining and Metallurgy

Anisotropic rock mass behaviour in high-displacement ground at CSA mine G.B. Sharrock1 and B. Chapula2

Affiliation: 1 Itasca Australia Pty Ltd, Brisbane, Australia.

2 CSA Mine, Cobar Management Pty, Cobar, Australia.

Correspondence to: G.B. Sharrock

Email:

gsharrock@itasca.com.au

Dates:

Received: 31 Jul. 2019 Accepted: 18 Sep. 2019 Published: January 2020

Synopsis This paper summarizes key findings from a 39-month study at CSA mine on factors controlling anisotropic ground behaviour in sublevel open stope access tunnels at depths of 1500–1700 m. The aim was to understand factors controlling high-displacement ground behaviour through numerical and empirical back-analysis at 45 damage sites over a 39-month period. Excavation orientation, rock mass matrix and foliation strength, and stress path were identified as the key parameters influencing tunnel damage and convergence. Tunnels driven parallel to foliation (i.e., along strike) experienced much higher levels of damage than those driven perpendicular to foliation. Drives at intermediate angles experience varying levels of damage, depending on the rock mass strength and stress. The stress path induced by mining was found to significantly affect the initiation and progression of damage in both tunnels and raises.

Keywords stoping, tunnelling, squeezing, bedding, high stress.

How to cite:

Sharrock, G.B. and Chapula B. Anisotropic rock mass behaviour in high-displacement ground at CSA mine.

The Southern African Insitute of Mining and Metallurgy DOI ID: http://dx.doi.org/10.17159/24119717/863/2020

This paper was first presented at the Deep Mining 2019 Conference, 24–25 June 2019 Misty Hills Conference Centre, Muldersdrift, Johannesburg, South Africa.

Introduction CSA mine is an underground copper mine located near Cobar, NSW, Australia, that currently produces around 1.2 Mt/a at 4% Cu. The CSA copper-lead-zinc orebodies consist of several steeply dipping parallel lenses that strike north-south. The orebodies occur within steeply dipping north-south trending shear zones which cut across the sedimentary rocks of the Upper Silurian-Lower Devonian Cobar Group (Tavakoli, 1994). The dominant historical and current mining method at CSA is sublevel open stoping (SLOS), currently implemented to 1700 metres below surface (mbs). The life of mine (LOM) plan has workings extending to at least 2100 mbs. Historically, above 9280 m below reference level (mRL) or 920 mbs, open stopes were relatively small (HR ≤ 4 m, span ≤ 15 m) to prevent crown and sidewall instabilities. The current mining method relies on single lift stopes 25 m in height (the sublevel interval is 25 m, transitioning to 30 m below 8610 mRL or 1590 mbs) mined under cemented backfill (10% Portland cement, UCS = 1.2 MPa) The crown and backfill spans are around 20 m or less. The stope sequence is centre in, top down, with hangingwall stopes leading to stress shadow footwall stopes and tunnels (Figure 1). This method has worked well to date, although increasing levels of buckling damage in the sidewalls and brittle damage in the backs are being experienced inside the closure pillar on the lower abutment and in perimeter drives driven parallel to the foliation.

Geology, stress, and rock mass strength Geology and rock mass classification The host rock mass at CSA comprises predominantly steeply dipping, thinly bedded siltstone. The bedding strikes north-northwest and dips west at 80°. The host rock mass also has a northerly trending axial planar cleavage that dips steeply east (80°). Within the siltstone unit, both bedding and cleavage are the dominant structures, with their intensity varying throughout the mine (Hosken Haren, and Winchester, 2006). A shear zone exists in the orebody (QTZ domains, Figure 2), and footwall (TSR domain, Figure 2). The modified tunnelling quality index (Q’) is used to classify the rock mass (Table I). The presence of shear zones affects rock mass quality (Q’ = 0.05–7.5), especially the joint alteration component of Q’. Most of the rock mass where there is no shear influence has Q’ > 4. In the ore zone, inside the shear zone, drives aligned with foliation and disturbed by the stoping stress abutment experience buckling failure and high deformation. High deformation is also experienced at lower stress The Journal of the Southern African Institute of Mining and Metallurgy

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Anisotropic rock mass behaviour in high-displacement ground at CSA mine

Figure 1—CSA mine, June 2018

Figure 2—Rock mass domains and damage sites (8610 mRL)

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Anisotropic rock mass behaviour in high-displacement ground at CSA mine Table I

Estimated material properties for geotechnical domains at CSA mine. Rock mass data was derived from logging of diamond drill-holes

Intact rock Roack mass (Median) Geotechnical domain Density sci (MPa) mi Ei vi sti Classification rating Average Dim (GPa) Dim (MPa) RQD Op GSI kg/m3* Host rock mass BC_Z28032017 CSR_North_16032017 CSR_South_16032017 QTZ_Central_02032017 QTZ_East_23032017 QTZ_West_23032017 LENS_EX_HG LENS_HG FGZ_170517 TSR_28032017

2800 2800 2800 2800 2800 2800 2800 2900 2900 2800 2800

120 120 120 120 120 120 120 150 150 25 120

12.0 8.0 10.0 10.0 10.0 10.0 8.0 14.0 14.0 8.0 7.0

68 68 68 68 68 68 68 84 84 11 68

*Anistropy ratio for all rock types = UCSmax/UCSmin = 1:1 xcut, 1:5 strike

0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.19 0.26

11 11 11 11 11 11 11 13 13 2 11

CSA/Itasca estimates

71 75 35 39 49 75 67 46 46 12 59

18.0 15.7 7.8 12.7 17.4 16.6 18.2 15.5 15.5 3.7 17.5

Joints Jr Ja

61 64 44 54 51 64 68 49 49 41 64

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

1.0 1.0 1.0 0.5 1.0 1.0 0.5 1.0 1.0 0.5 0.5

Values vary by level

Table II

Pre-mining stress directions and gradients at CSA mine Stress component

Magnitude Plunge (degrees) Trend (degrees) (MPa)

Stress @ 1000 mbs

S1:S2:S3 S1/S3 : S2/S3 : S3/S3 41:27:18

Sigma 1 (S1)

0.027 x depth + 13.5

15

278

41

Sigma 2 (S2)

0.019 x depth +7.5

20

185

27

Sigma 3 (S3)

0.018 x depth

64

41

18

Vertical (Szz)

0.027 x depth

–

–

38

2.3:1.5:1

Figure 3—UCS50 anisotropy for bedded siltstone (right) Cylindrical UCS specimen post testing; loaded perpendicular to foliation axis (After Sandy, 2011)

states in footwall drives aligned with the foliation. The intensity of the shear zone in the footwall drive is overshadowed by bedding. CSA has developed methods to identify, support, and measure ground conditions and deformation in these domains.

The mine is generally dry and aseismic.

Mechanical response at core scale The intact rock strength is generally greater than 100 MPa (Table The Journal of the Southern African Institute of Mining and Metallurgy

II), but even at the core scale the rock mass is highly anisotropic.

The bedding spacing varies both within and across domains and needs to be accounted for in accessing the mechanical response of the rock mass at both tunnel and stope scales. An example of the anisotropic response at UCS50 scale for bedded siltstone is shown in Figure 3. Estimates of the anisotropic intact UCS strength ratio for each domain are presented in the footnote to Table I. The anisotropic ratio at drive scale is unknown, but observations and VOLUME 120

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Anisotropic rock mass behaviour in high-displacement ground at CSA mine back-analysis suggests that the ratio may reduce significantly at the 1 m scale to around 2:1, rather than 5:1 as measured in UCS tests core.

Pre-mining stress state CSA has a rich history of stress measurements going back to the inception of the CSIRO HI stress cell, but the highly bedded nature of the rock mass makes obtaining valid stress measurements challenging. The accepted major principal stress direction is approximately east-west (see Table I), which agrees with the regional or stress province direction (Lachlan Orogen) documented in Lee et al. (2010). However, the depthstress gradient of S1 at CSA mine is somewhat lower than the stress province average (i.e., at 1000 mbs, S1 = 41 MPa at CSA compared with S1 = 55 MPa for the stress province average).

The stress province average is around 20% higher than the CSA measurements.

Figure 4—Total displacement: 8610 mRL (March 2017

CSA damage scale Extensive observational data for damage was collected at 45 sites across the eight levels (8790 to 8580 mRL) to help to understand the key drivers for damage. The first type of data is damage mapping as categorized in Table II, in conjunction with a damage mapping history for each site. The second is lidar laser scanner data, which was used to quantify tunnel convergence at selected tunnels on the lower levels of the mine (see Figure 4).

Observed failure mechanisms and controls Mining sequence A detailed empirical and numerical back-analysis of tunnel deformation was undertaken for the lower five levels, between 8790 mRL and 8580 mRL, over a 39-month period between

Table III

CSA damage mapping scale (After Sandy et al. 2010, Modified by CSA Damage criterion at CSA Mine

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Anisotropic rock mass behaviour in high-displacement ground at CSA mine January 2014 and March 2017 (see Figure 5). Within the backanalysis volume, stope spans and heights are around 20 m and 25 m respectively. Stopes were filled with cemented hydraulic fill or cemented paste fill. The mining sequence is a central pillar retreat, top down (see Figure 5). The stopes in the hangingwall are mined first, leading to stress shadow footwall stopes and tunnels in the footwall.

Observed damage and failure mechanisms The back-analysis was performed by firstly decomposing the 45 damage sites into damage domains across the eight levels of interest. A range of model and site investigation parameters within each domain was tabulated and both elastic and inelastic models were run using monthly mining stages to examine the key drivers for damage and to calibrate modelled-to-observed damage.

Initial calibrations attempted to match material properties for each geotechnical domain to damage without consideration of RQD variations within each domain. Using this approach, it was found that damage variations within domains could not be matched. This led to further consideration of the site investigation data and underground mapping. It was discovered that decomposition of each domain by RQD improved the match to observational data. As a result, damage partitions and associated material properties were developed for each domain. A key outcome of the back-analysis was a new appreciation of the importance of regular damage mapping and assessment of failure mechanisms, preferably each month or at least each quarter.

A number of failure configurations or behaviours can be observed in the site investigation data at damage sites. The damage mechanisms are categorized and described used the terminology of Hadjigeorgiou and Karampinos (2017) and Sandy, Gibson, and Gaudreau (2007).

Damage configuration 1: Perimeter drives and ventilation shafts in shear zones The most commonly observed damage mechanism is buckling in the sidewalls of tunnels driven near-parallel to bedding (e.g., perimeter drives and ore-zone strike drives). These tunnels experience much higher levels of buckling damage than those driven perpendicular to bedding (such as crosscuts). A typical scenario involves the formation of a hinge line or tensile fracture zone as seen in the right sidewall in Figure 6. The authors have observed hinge lines up to 8 m in length in exposures in crosscut sidewalls. Scanline mapping on the 8670 ore drive indicates Q’ values of 0.833–3.33 (RQD = 10, Jn = 3, Jr = 0.5, Ja = 2) and sidewall elastic vertical stress states in the order of 60 MPa. By contrast, scanline mapping on the 402 crosscut on 8670 level, which is not in a shear zone, indicates Q’ = 12.5 - 25 (RQD = 50, Jn = 3, Jr = 1.5, Ja = 2). No damage was record at this location.

For more purely bucking failures, the onset and intensity of buckling was found to vary with mining-induced stress and rock mass strength (defined by rock quality designation (RQD) and joint alteration). In particular, the mechanical response observed in perimeter and ore zone strike drives occurs at different stress thresholds (and stress paths) and seems to be confined to

Figure 5—Back-analysis model stages (1-month periods) The Journal of the Southern African Institute of Mining and Metallurgy

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Figure 6—Significant deformation (level 3–4 damage) along the 8670 ore drive

moderate to poor strength rock masses affected by shears, with very little failure of the backs.

Perimeter tunnels aligned with foliation in moderate stress locations experience buckling on foliation. This is a consideration for access drives in moderate to poor quality rock masses or shear zones (e.g., the TSR domain, Figure 2). In these domains significant deformations occur due to stress changes (stress rotation and changes in shear stress) induced by the stope mining front (see Figure 7a). The typical stress path for a perimeter tunnel starts with high concentrations of stress in the excavation back (and to a lesser extent the sidewalls) that occurs when the isolated excavation is first formed. As the mining abutment approaches, shear stress around the excavation rotates and increases, inducing shear and tensile failure in bedding in the excavation sidewalls and corners (see Figure 7a). Stress rotation (Figure 7a) in particular is thought play a key role in

the extension of the zone of damage in tunnel sidewalls, as evidenced by the cessation of deformation after the mining front has past (at least in moderate to poor quality rock masses). After the mining front has passed, deformations are locked in, and shear stress reduces significantly. Deformation typically ceases if the excavations are adequately supported. In badly damaged ground, destressing can accompany increased support loads.

Similar deformation responses to stress and strength occur in ventilation raises located in the footwall, which are typically supported heavily with bolts and fibre-reinforced shotcrete (FRS) (Figure 8).

More rarely, some perimeter drives are excavated in very poor quality rock masses that deform regardless of excavation orientation (see Figure 9a), even from the isolated stress state (e.g. TSR, BCZ, QZT_West domains). These domains typically have RQD < 25 in addition to substantial alteration (such as talc).

Figure 8—Significant deformation (level 3–4 damage) on the eastern side of the 8700–8670 FAR

Figure 7—(a) Major principal stress (looking north), (b) plastic strain

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Anisotropic rock mass behaviour in high-displacement ground at CSA mine

Figure 9 â&#x20AC;&#x201C; IMASS model incorporating matrix and joint strength to capture excavation damage

The authors assessed the mechanical response of the tunnel to foliation, using the Itasca Model for Advanced Strain Softening (IMASS) constitutive model available in FLAC3D, which was developed by Itasca to simulate rock mass softening, and strength anisotropy due to embedded planes of weakness within a continuum model (Ghazvinian et al.). Within the IMASS model, zone-based matrix and joint properties are specified.

Each of these property sets (matrix and ubiquitous joints) can fail in tension and shear independently of one another.

It is noted that the ubiquitous joints provide a weakness orientation in each model zone. These joints however are not an explicit representation of individual discontinuities, such as those included in 3DEC. The shear and tensile strength of the The Journal of the Southern African Institute of Mining and Metallurgy

ubiquitous joints can be specified, but stiffness properties are not assigned. For the CSA analyses, parameters in this constitutive model were calibrated to damage observations. The application of IMASS allows the effect of tunnel trend and foliation direction on damage to be captured. At CSA, the matrix strength is most relevant to drive backs (especially on the mining abutment) or the side walls of tunnels driven perpendicular to foliation or where foliation is clamped. By contrast, the joint strength is most relevant to tunnels driven parallel to foliation. Both the matrix and joint strength was calibrated for each geotechnical domain or RQD domain.

The two key drivers for damage in perimeter drives, found to have good correlation with damage, were the strength of the VOLUME 120

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Figure 10—Footwall drive in very poor-quality rock mass at 1700 m (drive trending north)

joints (Figure 9b), and the vertical stress state in the excavation sidewall (Figure 9a), (normalized to drive orientation). The impact of excavation direction for typical footwall drives in a very poor quality rock mass in north- and east-trending drives is shown in Figures 10 and 12 respectively.

It is noteworthy that the elastic vertical stress in the excavation sidewalls (see Figure 10a) is approximately 60 MPa and the major principal stress in the excavation backs is around 100 MPa (see Figure 10c). In contrast, plastic strain (matrix damage) in the excavation backs is not significant (Figure 10b) and joint plus matrix damage (Figure 10d) in the excavation sidewalls leads to sidewall failure and bulking.

Damage configuration 2: Ore zone drives In contrast with perimeter drives, damage in strike drives in the ore zone is more strongly influenced by the mining stress abutment and shear zones (see Figures 11a and 11b). For example, scanline mapping on the 8540 level, which is not in a shear zone, indicates Q’ = 13–200. It is noteworthy that on the abutment, high stress states exist not only in the backs of tunnels, but also in the sidewalls, leading to two modes of damage. The first is buckling of excavation sidewalls (and pillar noses) due to overstressing of the foliation. As with the perimeter strike drives, the intensity of buckling damage was found to vary with rock mass quality and vertical stress, rather than with major principal stress. A good example of this mode of deformation is shown in Figure 6, where significant deformation along the ore drive in 8670 level (1530 mbs) is observed. This drive squeezed

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from 5 m to 3 m (40% convergence), necessitating rehabilitation of the drive with ground support and reinforcement installed floor-to-floor. Before deformation, the drive had 50 mm fibrecrete, 2.4 m resin bolts (1.1 m × 1.4 m spacing) and 5.6 mm welded wire mesh as primary support and 6 m single strand cable bolts (2.2 m × 2.2 m spacing) as secondary support.

The second damage mode is brittle failure and block rotation in the excavation backs (across the foliation) near brows in high-stress locations. This mode of damage has historically been rare but is becoming more common as the mining depth increases, especially inside the closure pillar. The closure pillar is established when two stope fronts approach each other. To date, very little mining-induced seismicity has been recorded, and it is noteworthy that the rock mass is generally weak and no rockbursts or significant fault slip events have been recorded.

Strike ore drives are typically in ore lenses (CSR_N or CSR_S domains). The rock mass compressive strength is generally higher than for perimeter drives in shear zones, and numerical back-analysis suggests it is well approximated by the global strength of Marinos and Hoek (2000).

Comparison with empirical data-sets

Mercier-Langevin and Hadjigeorgiou (2011) presented a Hard Rock Squeezing Index for underground mines based on case studies from mining operations in Australia and Canada.

Hadjigeorgiou and Karampinos (2017) note that this index can provide a first indication of the potential for squeezing and the long-term strain level based on ranges for the foliation spacing and the stress-to-strength ratio.

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Anisotropic rock mass behaviour in high-displacement ground at CSA mine

Figure 11â&#x20AC;&#x201D;8160 mRL, March 2017. (a) Vertical stress, (b) joint shear strain

Seven case studies from CSA and Mount Lyell (after Sharrock and Cuello, 2016) have been added to the chart (see Figure 12).

In general, the magnitude of damage estimated from the chart is in broad agreement with underground observations. However, a key observation at CSA is the importance and impact of joint alteration, which is not accounted for in the chart. Future work at CSA will seek to better understand the effects of alteration.

Conclusions Excavation orientation, rock mass matrix and foliation strength, and stress path are key parameters influencing tunnel damage The Journal of the Southern African Institute of Mining and Metallurgy

and convergence at CSA mine. Tunnels driven parallel to foliation (i.e. along strike) experience much higher levels of damage than those perpendicular to foliation. Drives at intermediate angles experience varying levels of damage depending on rock mass strength and stress.

In most of the damage sites, high deformations correlate with shear zones and/or high-stress states or stress rotation and deconfinement. However, more work needs to be done to understand the variability within domains, the role of alteration, and the impact of stress path and stress rotation. A key finding from back-analysis of squeezing and buckling ground behaviour VOLUME 120

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Figure 12—Hard rock squeezing index, after Hadjigeorgiou and Karampinos (2017) updated with CSA and Mount Lyell data (8160 mRL, March 2017)

at CSA is the need to look beyond RQD as a stand-alone metric for identifying high-deformation ground conditions. In particular, better observational data from scanlines and damage sites is required to build an understanding of the conditions and mechanisms controlling anisotropic ground behaviour. This work is ongoing and is explored further in Chapula and Sharifzadeh (2019).

Acknowledgements The authors acknowledge the contributions of Jan Jacobs, Tim Brettell, Cameron Tucker, and Patrick Mukwindidza, who contributed substantially to the collection, analysis, and interpretation of observational data at CSA mine.

References Chapula B. and Sharifzadeh M. 2019. Strategies for managing large deformation at CSA Underground Mine. Proceedings of the 14th International Congress of Rock Mechanics, Foz do Iguaçu, Brazil, 13–18 September 2019.

CMPL. 2017. CSA mine, ground control management plan, May 2017. Cobar Management Pty Ltd, Cobar, NSW. Ghazvinian, E., Garza-Cruz, T., Fuenzalida, M., Bouzeran, L., Cancino, C., Cheng, Z., and Pierce, M. 2020. Theory and Implementation of the Itasca Constitutive Model for Advanced Strain Softening (IMASS), In prep: MassMin 2020, Santiago, Chile.

Hadjigeorgiou, J. and Karampinos, E. 2017. Design tools for squeezing ground conditions in hard rock mines. Deep Mining 2017: Proceedings of the Eighth International Conference on Deep and High Stress Mining. Wesseloo, J. (ed.).

Australian Centre for Geomechanics, Perth. pp. 693–706.

Hosken, J., Haren, E., and Winchester, A. 2006. Resource modelling in an evolving mine – CSA Mine, Cobar, New South Wales. Proceedings of the Sixth International Mining Geology Conference, Darwin, NT. 21–23 August 2006.

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Dominy, S. (ed.). Australasian Institute of Mining and Metallurgy, Melbourne. pp. 153–166.

Lee, M., Mollison, L., Campbell, A., and Litterbach, N. 2010. Rock stresses in the Australian continental tectonic plate – Variability and controls. Proceedings of the 11th IAEG Congress – Geologically Active New Zealand, Auckland, September 2010. Chin, C.Y., Massey, C.I., McMorran, T.J., Pinches, .G.M., and Williams, A.L. (eds.). CRC Press, Boca Raton, FL. Marinos, P. and Hoek, E. 2000. Predicting tunnel squeezing problems in weak heterogeneous rock masses. Tunnels and Tunnelling International., Part 1, November, Part 2, December. https://www.rocscience.com/assets/ resources/learning/hoek/Predicting-Tunnel-Squeezing-Problems-in-WeakHeterogeneous-Rock-Masses-2000.pdf Mercier-Langevin, F. and Hadjigeorgiou, J. 2011. Towards a better understanding of squeezing potential in hard rock mines. Mining Technology, vol. 120, no. 1. pp. 36–44.

Sandy, M. 2011. Strength Anisotropy in Foliated Rocks, Unpublished AMC Consultants Presentation to CSA. Sandy, M.P., Gibson, W., and Gaudreau, D. 2007 Canadian and Australian ground support practices in high deformation environments. Challenges in Deep and High Stress Mining. Potvin, Y., Hadjigeorgiou, J., and Stacey, D. (eds). , Australian Centre for Geomechanics, Perth. pp. 297–311.

Sandy, M., Sharrock, G., Albrecht, J., and Vakili, A. 2010. Managing the Transition from Low Stress to High Stress Conditions, Proc 2nd Australasian Ground Control Conference in Mining, Sydney 2010.

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Addressing misconceptions regarding seismic hazard assessment in mines: b-value, Mmax, and space-time normalization J. Wesseloo1 Affiliation: 1 Australian Centre for Geomechanics, The University of Western Australia, Australia.

Synopsis Seismic hazard assessment, in some form or another, has formed part of seismic risk management in seismically active hard-rock mines for decades. Some misconceptions, however, exist in the mining industry which may lead to errors in interpretation and poor risk management decisions. This paper addresses some misconceptions the author has encountered in the mining industry. This is done by exploring the meaning and implications of the frequency–magnitude distribution. The meaning of Mmax, the methods of assessing it, and the topic of space and time normalization necessary for the evaluation of seismic hazard, are also addressed. The scope of this paper does not include the evaluation of strong ground motion exceedance which also forms part of the evaluation of seismic hazard at mine sites.

Correspondence to: J. Wesseloo

Email:

johan.wesseloo@uwa.edu.au

Dates:

Received: 29 Jun. 2019 Revised: 5 Dec. 2019 Accepted: 11 Dec. 2019 Published: January 2020

Keywords Seismic risk, frequency–magnitute distribution, normalization probability.

How to cite:

Wesseloo, J. Addressing misconceptions regarding seismic hazard assessment in mines: b-value, Mmax, and space-time normalization.

The Southern African Insitute of Mining and Metallurgy DOI ID: http://dx.doi.org/10.17159/24119717/855/2020

This paper was first presented at the Deep Mining 2019 Conference, 24–25 June 2019 Misty Hills Conference Centre, Muldersdrift, Johannesburg, South Africa.

Introduction At any seismically active mine, considerable effort is invested into the effective management of seismic risk (see Potvin et al., 2019), of which seismic hazard assessment is, of course, a fundamental component. Over many years I have come across several misconceptions regarding the assessment of the seismic hazard which adversely affect the standard of seismic risk management in our industry.

Some of these misconceptions are widespread and deeply rooted, while others crop up from time to time and seem to migrate through the industry. The aim of this paper is to address some of those misconceptions.

The frequency–magnitude (FM) distribution is foundational to understanding seismic hazard. It appears, however, that many of the misunderstandings regarding seismic hazard arise from inadequate understanding of the meaning and implications of the FM distribution. For this reason, a large proportion of this paper will be devoted to the FM distribution and its implications. The fact that ‘Mmax’ is used for several different concepts, further creates confusion and misunderstanding. Another topic that does not seem to be well understood is that of normalization of hazard, with respect to space and time, and the related issue of separating sources of seismicity with different behaviour.

The assessment of seismic hazard in mines also includes the evaluation of strong ground motions at specific locations. This topic is, however, excluded from the scope of this paper.

What is the Gutenberg–Richter relationship really?

It appears that many misconceptions in the mining industry stem from an inadequate understanding of the FM distribution and its implications. Many rock engineers use the FM chart without realizing that it is simply a reverse cumulative distribution1 of magnitude, with the vertical axis plotted on a log scale.

The straight line fit, or any other curve fitted to the data, is therefore simply a statistical best-fit model and is conceptually the same as, for example, a normal distribution fitted to UCS data (see Figure 1).

1

lso referred to as Complementary Cumulative Distribution or Inverse Cumulative Distribution. The term ‘Inverse Cumulative Distribution’, A however, is also used to refer to the quantile function.

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Figure 1—Graphical representation and the statistical best-fit model of UCS data and seismic magnitude data

The model most often used for seismic magnitude distribution is the Gutenberg–Richter (GR) relationship (Gutenberg and Richter, 1944):

Where Nmmin is the number of events with magnitude greater than mmin F’(M) is the reverse cumulative distribution function

[1]

It is interesting to note that F’(M) in Equation 2 is simply a different formulation of the commonly used negative exponential distribution, translated to start at mmin instead of zero.

The open-ended GR relationship (Equations [1] and [2]) predicts a non-zero probability of physically impossible magnitude sizes, (i.e., the probability of a Richter magnitude > 10 is not zero!). For this reason, equations that truncate at large magnitudes are preferred, for which several different relationships have been proposed (Utsu, 1999). In mining, the truncated Gutenberg–Richter (TGR) relationship (Page, 1968) is often used. Equation [4] rewrites Page’s formulation to follow the general formulation that is commonly used by rock engineers, as presented in Equation [3].

Which we can write as: [2] where: N is the number of events with magnitude ≥ M M is the event magnitude a is the coefficient quantifying the number of events b is the coefficient quantifying the log relative occurrence of smaller to larger events In the case of seismic magnitude data, however, limitations in the system sensitivity result in small events not being recorded.

For statistical and probabilistic analysis, one can therefore only use the data above the magnitude of completeness, mmin, with the statistical density and cumulative function being limited to the magnitude ranges greater than mmin. We can write the GR relationship in Equation [2], with respect to the reverse cumulative distribution function, as follows:

with the reverse cumulative distribution written as:

[4]

[3] where Nmmin is the number of events with magnitude greater than mmin MUL is the upper truncation magnitude of the distribution.

Similar to Equation [2], Equation [4] is a different formulation of a truncated and translated negative exponential distribution.

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Addressing misconceptions regarding seismic hazard assessment in mines The cumulative distribution function, F(M) and the probability density function, f(M) for the TGR can be written as follows:

[5]

It is important to note that any statement regarding the expectation of specified event magnitudes is dependent on the number of events. For any such statement to be meaningful, it is necessary to specify the number of events applicable to this statement. In this paper, the short notation, N@M, is used; for example, N@-1=100 or N@0 = 1, and the a-value in Equations [1] to [4] is equal to log(N@0).

Distribution of the expected largest event [6]

The Mmax confusion In the mining industry, the term ‘Mmax’ is used for several different concepts, with the loose meaning of ‘expected largest event’ associated with it. These concepts are (see Figure 2): (a) the largest recorded event in a dataset (Xmax) (b) the value of the fitted GR relationship at N = 1 (a/b) (c) the distribution of the largest expected magnitude (fmax) (d) the largest physically possible event (Mmax) (e) the upper truncation of the FM distribution (MUL).

Each of these is, in some way or another, being used as hazard indicators and they are discussed in the following paragraphs. Generally, no distinction is made between Mmax and MUL, and the two concepts are mostly used interchangeably.

Conceptually Mmax has a physical meaning, while MUL carries only the abstract meaning of being an upper limit of a probability distribution. I find it useful to keep these two concepts separated and will discuss this in more detail further on in the paper.

Due to the stochastic nature of seismicity, the largest event magnitude within a given number of events is a distribution and cannot be captured with a single number. This distribution is represented as a colour spectrum at N = 1 in Figure 2. In this paper, the probability density, cumulative probability and reverse cumulative probability functions of the distribution are respectively referred to as fmax, Fmax, and F’max.

Figure 2—FM distribution of seismic magnitude The Journal of the Southern African Institute of Mining and Metallurgy

The two parameters a/b and MUL, shown in Figure 2 are important parameters in the assessment and communication of seismic hazard (Kijko and Funk, 1994; Jager and Ryder, 1999; Hudyma and Potvin, 2004; Hudyma, 2008; Mendecki, 2008; Hudyma and Potvin, 2010; Mendecki, 2012; Mendecki, 2013b).

It appears though, that undue emphasis is sometimes placed on these parameters, which do not fully capture the seismic hazard.

Consider the distribution of the maximum expected event within N@-1.5 = 4000, shown in Figure 2; the value of MUL defines only the upper limit of the F(M) and Fmax(M) distributions and the a/b-value describes only one point on those distributions. The whole body of the distribution, however, describes the hazard.

The distribution of the expected largest event can be obtained directly from the FM distribution. The cumulative distribution function of the largest magnitude within n events is given by (Gibowicz and Kijko, 1994): [7] where Fmax(M,n) is the cumulative distribution function of the magnitude of the largest event within n events n is the the number of events with magnitude ≥ mmin The probability density function of the largest event within n events can be obtained as the derivative of Fmax and is as follows: [8]

where fmax(M,n) is the probability density functions of the magnitude of the largest event within n events F(M), f(M) is the the cumulative distribution and probability density functions of the FM distribution (e.g.

Equation 5 and 6) n is the the number of events with magnitude ≥ mmin It is well-known that the intensity of dynamic waves attenuates sharply with distance. Using the Canadian Rockburst Handbook formulation (Mining Research Directorate, 1996), one can estimate that the body wave peak particle velocity (ppv) resulting from a Richter magnitude (ML) of 3 at a distance of 500 m is similar to that from a ML 0.5 at a distance of 20 m.

Assuming a b-value of 1, one would expect about 315 events of ML > 0.5 for every single event of ML > 3. When considering the fact that the workforce is exposed to the occurrence of many more small events at close proximity, it is clear that the body of the distribution, and not only the upper limit or the mode of the distribution, is important. This is evidenced in the fact that it is becoming more common to install face support in development headings to protect the workforce against the effect of events much smaller than the MUL. VOLUME 120

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Addressing misconceptions regarding seismic hazard assessment in mines The consequence of smaller events is, of course, expected to be much smaller than that of an Mmax event. One useful way to quantify the hazard, therefore, is to express it as the probability of exceeding a large event, for example, ML1, ML2, and ML2.5 are commonly used, which for the illustration in Figure 2 are about 100%, 70%, and 30% respectively.

Xmax By its very nature, the largest event in the data-set is a property of a data-set under consideration. For this reason, I prefer the convention employed by Gibowicz and Kijko (1994), and Kijko and Funk (1994), who refer to this value as Xmax (the maximum obs is also used in literature. value of set X) although Mobs or Mmax The value of Xmax is an indicator of hazard level in that it provides a lower bound of the largest event that can be expected in future (MUL > Xmax). Unless there is a significant and proven change in the conditions, the only defensible assumption is that an event larger than Xmax can occur. This condition may be a significant change in the seismic regime that can be brought about by, for example, a change in the mining method or when mining moves from strong brittle ground into squeezing conditions.

Xmax is, of course, highly dependent on the size and representativeness of the subset of data under consideration and Xmax of the subset loses significance as a hazard indicator when a subset is temporally or spatially too small. Xmax as a hazard indicator captures only the historical experience and does not in any way account for the stochastic nature of seismicity.

THE a/b-value The value of a fitted GR relationship at N = 1 is sometimes also referred to as ‘a/b’ (Hudyma, 2008) since it is equivalent to the ratio of the a and b parameters of the GR relationship (Equation [1]). It is important to note that a/b is a property of the fitted statistical model and not of the underlying data. The a/b-value is commonly used as an indicator of seismic hazard level with the meaning of ‘the largest expected event’ assigned to it.

It appears that this practice of interpreting a/b as the maximum event size is reinforced by the misunderstanding of the FM graph plotted for historical data (Figure 2). As the minimum value of the logarithmic y-axis is generally plotted as unity, and since no fractions of events can occur, this is interpreted as the ‘end of the graph’. The GR relationship, however, is not a representation of the discrete events that occurred but a statistical best-fit model describing the relative frequency/probability of different sizes of event. There is no fundamental reason to stop the graph at N = 1. Interpreting the reverse cumulative distribution to terminate at N = 1 ignores the upper tail of the distribution, similar to the red line illustrated in Figure 3.

From the TGR distribution with the number of events determined by a, the probability of exceeding a/b is given by the following equation derived in Appendix A and plotted in Figure 4: [9] Which, for the open-ended GR relationship, reduces to the following: [10] where: Xmax is the largest experienced event b is the coefficient quantifying the log relative occurrence of smaller to larger events a is the coefficient quantifying the number of events N@0 = 10a k is the Fraction by which MUL is larger than a/b, i.e. MUL = k ∙ a/b For an open-ended GR relationship, there is a > 63% chance of exceeding a/b. The probability is less for the truncated distribution. For situations where MUL is large compared to a/b, the probability of exceeding a/b is large and the probability of exceeding a/b reduces for a/b closer to MUL. The probability of

Figure 3—Truncation of statistical distribution at N = 1 to the largest point in the data-set for both UCS data and seismic magnitude data

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Figure 4—Probability for exceeding a/b

exceeding a/b where a/b ≥ MUL is, per definition, zero. Note that these statements refer to the probability of exceeding a/b within the number of events determined by the a, i.e. N@0 = 10ª.

The a/b-value is an important parameter and it has a very clear meaning not generally appreciated. The a/b-value is the mode of the distribution of the largest event (the mode of fmax) (Figure 5). This is true for both the GR and TGR relationships (Appendix A).

The a/b-value is often used without consideration of spatial or temporal normalization, in which case it is a function of the subset of data under consideration and it loses any meaning as a hazard indicator. This will be discussed in greater detail further in the paper.

Mmax and MUL In physical terms, Mmax is used to define the region-characteristic maximum possible event magnitude, or, as the upper limit of event magnitude for a given region (Kijko and Singh, 2011). In other words, the largest magnitude that physical conditions will allow. In crustal seismology, this value is generally assumed to be constant for a particular seismic source zone. However, in

the mining environment, the value of Mmax is not constant and is influenced by a number of factors, for example, rock mass conditions, mining-induced stress state, the mining sequence, and mining layout. In addition, Mmax is expected to increase with the extraction ratio (Mendecki, 2012).

As mentioned before Mmax and MUL, are mostly used interchangeably. Conceptually Mmax has the previously mentioned physical meaning, whilst MUL carries only the abstract meaning of being an upper limit of a probability distribution. I find it useful to keep these two concepts separate as one may choose to use a large value for MUL, say M6, without implying that Mmax = 6. The only implicit statement is that Mmax < 6. Due to several difficulties which will be discussed in further detail in the following paragraphs, estimates of Mmax is subject to a great deal of uncertainty. For the purpose of hazard assessment and management, however, choosing a conservative but realistic value for MUL will suffice.

When assessing the probability of exceeding a specified magnitude P[M > Mt], underestimation of MUL with an error of δ (MUL = Mmax - δ) leads to larger errors than overestimation of MUL by the same amount (MUL = Mmax + δ). Underestimating MUL is always optimistic, whilst overestimation is always conservative (Wesseloo, 2018). For the purpose of hazard assessment it is, therefore, prudent to use values for MUL that are deliberately conservative.

Estimating Mmax and MUL Wesseloo (2018) suggested the use of several methods discussed

by Kijko (2004) to calculate Mmax plus the associated standard deviation, and assigning the maximum of these values to MUL. [11]

Figure 5—The a/b-value as the mode of distribution of the largest events within a given number of events The Journal of the Southern African Institute of Mining and Metallurgy

where: MUL is a conservative but realistic upper limit for fmax.

Mmax i is the largest magnitude that physical conditions will allow estimated with method i. is the standard deviation of the estimation of Mmax i ∆i The reliable and robust estimate of Mmax is not a trivial task and several researchers have invested considerable effort in finding reliable estimates from seismic event catalogues, with most of the effort directed at application in crustal seismology (e.g. Kijko and Funk, 1994; Kijko, 2004; Lasocki and Urban, 2011; Kijko, 2012). These methods, based on record statistics, aim to estimate the largest possible value based on the recorded data.

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Addressing misconceptions regarding seismic hazard assessment in mines The simplest and most well-known method for estimation of MUL is the Robson–Whitlock method formulated as follows (Kijko, 2004): [12] where Xmax and Xmax-1 are the largest and second-largest recorded events Among the other more common methods discussed by Kijko (2004) and (Kijko and Singh, 2011) are the Tate–Pisarenko, Kijko–Sellevoll, Order statistics, and Robson–Whitlock–Cooke and Cooke 1980.

It is important to note that the reliability of the estimate of Mmax is highly dependent on the number of events on which this assessment is based. This dependence is investigated using Monte Carlo analysis on synthetically generated data-sets sampled from a specified FM distribution. For this analysis, Mmax = 2.5, 3.5 and 4.5, b = 1, and a-values from 1 to 4.5 (N@0 = 10 to 32 000) were used. The results for the Robson–Whitlock method are shown in Figure 6. The value for Mmax is largely underestimated, with an underestimation of more 80% for an a/b < Mmax – 1.

It is clear from the analysis that reasonably accurate estimates for Mmax can only be obtained from large data-sets (N@0 > 1000 for Mmax = 2.5 N@0 > 10 000 for Mmax = 4.5). This implies that when the FM relationship is evaluated for smaller data-sets, the estimation Mmax should not be limited to that small subset of data.

Artificial Black Swan events The effect of variability According to the Financial Times Lexicon, a Black Swan

event is ‘An event or occurrence that deviates beyond what is normally expected of a situation and that would be extremely difficult to predict.’ Since the MUL is the upper truncation of the FM distribution, any event with magnitude greater than MUL is, per definition, assigned a zero probability of occurrence.

Underestimation of MUL, (MUL < the true Mmax), will assign a zero probability to magnitudes that could reasonably be expected, should the MUL > the true value of Mmax.

Consider the following example based on synthetic magnitude distribution data. Using random deviate sampling, magnitude values were sampled from a TGR relationship with the following properties: MUL = 4, mmin = -2, b = 1, and N@-2 is 100 000, i.e. a = 3. The results of several different approaches for estimating MUL are shown in Figure 7.

Each of the sub-figures shows the FM distribution data; the best-fit GR (blue) and TGR (red) relationships. The probability distribution of the largest event for the given TGR relationship is shown as a spectrum at N = 1. The only differences between the sub-figures are the MUL value and the resulting changes in the TGR relationship and the distribution of the largest events, fmax, derived from it.

In Figure 7a, the value of MUL = Mmax obtained with the Robson–Whitlock method (Equation [12]). In Figure 7b, MUL is taken as the maximum of all of the methods listed in the previous section. In Figure 7c, the MUL is taken as the maximum of all the aforementioned methods with an added standard deviation for each calculation, according to Kijko and Singh (2011), assuming a magnitude resolution of 0.1. Finally, in Figure 7d, MUL = the true Mmax = 4 is used. Even with MUL calculated as the maximum of the mentioned methods (Figure 7b) or with Equation [11], the probability of M > 3.5 is zero and 12% respectively whilst the actual value is 20%.

Figure 6—Uncertainty in the estimation of Mmax using the Robson–Whitlock method for different a-values, b = 1 and a true Mmax of (a) 2.5 (b) 3.5 and 4.5

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Figure 7—Comparison to probabilistic evaluations resulting from different values of MUL.taken as (a) the value Robson–Whitlock estimate. (b) the maximum of Tate–Pisarenko, Kijko–Sellevoll, Order statistics, Robson– Whitlock–Cooke and Cooke 1980 methods. (c) the maximum of all the aforementioned methods with an added standard deviation for each calculation assuming a magnitude resolution of 0.1. (d) the true value of Mmax = 4

The effect of sensor limitations

Apart from the underestimation of Mmax that may occur simply due to variability in the data, underestimation may also result from limitations in the lower frequency limit of sensors. Morkel and Wesseloo (2017) described the problem occurring when the lower frequency limit of the sensors is not sufficiently low to record the low frequency content of large events and no correction for this effect is made. When this occurs, it will lead to the under-recording of the moment (and potency).

Magnitude scales dependent on moment are thus susceptible to the underestimation of large magnitudes. This includes moment magnitude and magnitude scales defined as a function of both energy and magnitude. Since the estimation of energy is not sensitive to the under-recording of low frequencies (Boore, 1986), this effect is less pronounced for magnitude scales based on both energy and moment. Mendecki (2013a) performed a quick survey of 100 mines using IMS systems and found that 25% used a moment-based magnitude scale while the rest used a scale defined as a function of both energy and moment.

For simplicity, the following discussion is limited to moment magnitude.

As a result of the under-recording of the low-frequency content and subsequent underestimation of the moment magnitude, the FM distribution of the recorded data exhibits a nonlinear (on the log-linear scale) relationship, as shown in Figure 8.

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Figure 8a shows the FM distribution of recorded data from a mine network with only 50 Hz sensors. The downward curvature of the distribution is not a result of the underlying statistical behaviour of moment magnitude, but of the under-recording of the moment by the sensors. Also shown in the figure are two theoretical lines: the straight GR relationship assumed as the true distribution of moment magnitude; and a theoretical assessment of the effect of under-recording using analytical formulations (Boore, 1986; Di Bona and Rovelli, 1988; Mendecki, 2013a; Morkel and Wesseloo, 2017). The effect of different lower frequency limits of the sensors is illustrated in Figure 8b. The amount of under- recording for different moment magnitudes and lower frequency limits are shown in Figure 9.

For databases subjected to under-recording, underestimation of of Mmax will result from the use of the statistical methods. To illustrate this problem, consider the following scenario plotted for N@0 = 300 in Figure 10. A seismic system with sensor lower limits of 14 Hz recording events from a seismic source with b = 1, Mmax = 3. Figure 10 shows the assumed FM distribution as a blue line, and the synthetic events sampled from that distribution as light grey. The dark blue points are adjusted for the frequency limit of the sensors, according to Boore (1986).

The calculated value of Mmax, in this case, is 2.53 which is smaller than the actual value of the largest experienced event on which the assessment is based, M2.68. The red line shows the truncated GR relationship based on the recorded events.

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Figure 8—The effect of sensor frequency limits on the FM distribution of recorded seismic datasets, a) database and theoretical estimation of a 50 Hz system, b) theoretical estimation of moment magnitude under-recording for different lower frequency limits (Morkel and Wesseloo, 2017)

Figure 9—Under-recording of moment magnitude for different magnitudes and lower frequency limits

Figure 10—Synthetic data with adjustments for frequency under-recording, lower frequency = 14 Hz and a static stress drop of 0.1 MPa

Similar responses also occur for other frequency limits but the magnitude range at which the deviation becomes significant, differs. The lower the frequency limit of the sensor, the larger the magnitude that will be adequately recorded.

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To further quantify the effect of the lower frequency of sensors on the estimation of Mmax, a Monte Carlo analysis was performed, similar to that for which the results are shown in Figure 6. For this analysis, a lower frequency limit of 14 Hz and a static stress drop of 1 MPa are assumed. Excluding the added effect of the lower frequency of the sensor, these two analyses are the same. The results of this analysis are shown in Figure 11.

The two sub-figures in column (a) display results for Mmax = 2.5, (b) for Mmax = 3.5, and (c) for Mmax = 2.5. The top figure in each case presents the results for the Robson–Whitlock method. The results shown in the bottom figure in each case are obtained with Equation [11] using the Tate– Pisarenko, Kijko–Sellevoll, Order statistics, Robson–Whitlock–Cooke and Cooke 1980 methods.

It is clear from the comparison between Figure 6 and Figure 11 that the under-recording of moment due to sensor limitation can result in errors in the estimation of Mmax, which are always optimistic and can be significant.

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Figure 11—The effect of lower frequency limit on the estimation of Mmax. Lower frequency = 14 Hz, b=1, static stress drop = 1 MPa. The top three figures present the results for the Robson–Whitlock method. The bottom three figures show the results from Equation [11] using the Tate–Pisarenko, Kijko–Sellevoll, Order statistics, Robson–Whitlock–Cooke and Cooke 1980 methods

the mine (Figure 9). In lieu of this, corrections may be applied to the recorded values to compensate for the effect of the sensor under-recording (Morkel and Wesseloo, 2017).

Spatial distribution of seismic hazard Probabilistic hazard calculation is commonly performed on data within spatial filters. Such spatial filters are often delineated with respect to mining infrastructure, often with arbitrary size. Such arbitrarily chosen volumes can have a significant influence on the assessment and may influence decision-making.

The sensitivity of hazard assessment to arbitrarily chosen volumes relates to the spatial distribution of b-value, the spatial distribution of events, and the difference in volume for these arbitrary spatial filters.

The influence of the spatial distribution of b-value It is important to note that, when different seismic sources with different b-values are lumped together and the hazard calculated, the total hazard will be different from that when calculating the total hazard based on the separate sources.

To illustrate this point, consider the idealized case illustrated in Figure 12 with a square subdivided into equal sub-areas on the left-hand side. On the right-hand side, the whole area is evaluated as a single unit. By way of analogy, these squares represent a mining area. For this illustration, we would like to answer the following question:

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For the purpose of this illustration, we will assume a constant event rate.

An approach often used in mining is to evaluate the whole area, A, (for example a large mining block or corridor) obtaining the a- and b-values for the whole area, and calculate the required probability value using the following equation.

[13] where m is a specified threshold magnitude An alternative approach would be to calculate the a- and b-values for each of the sub-areas and calculate the overall probability as follows: 0The b-value for the combined area can be obtained from that of the sub-volumes as follows: [14] [15] [16] where: ai, bi = a- and b-values for sub-volume i It can be shown that for cases where the b-value over area A is constant, i.e. where bi = bA, the two approaches yield the same VOLUME 120

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Figure 12—Illustration of the difference between evaluating the seismic hazard as a single unit and for smaller units with constant b-value

Figure 13—The influence of volume size on the evaluation of hazard (a) a/b distribution for uniform volume size and (b) probability of exceeding M2.5, MUL = 4, mmin = 0

result. However, where this is not the case, the first approach does not yield the correct answer. For the example, in Figure 12 the difference between the two approaches leads to a difference of 9% probability of exceeding M2.

This illustration shows that the probabilistic evaluation representative of the volume under consideration can only be achieved by integration of the results obtained for each subvolume where the sub-volumes are small enough to represent a volume with constant b-value.

The influence of the volume of spatial filters

For a given b- and MUL value, the number of events within a spatial filter will determine the seismic hazard for that spatial filter. The effect of hazard quantification and comparison between arbitrarily chosen spatial filter volumes without volume normalization is illustrated in Figure 13 and Figure 14. The square area shown on the left-hand side in Figure 13 and Figure 14 represents a whole mining area. In the left-hand side figures, the whole area consists of a hundred equally sized sub-areas which, for this example, each has a uniform distribution of events. The right-hand side figures display the same scenarios as that of the left-hand side figures, except that in these cases the

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whole mining area is subdivided into arbitrarily chosen spatial filter areas.

In Figure 13, the whole mine has the same seismic event rate of N@0 = 10 and b = 1. As a result, the spatial distribution of hazard is uniform throughout the whole mine with an a/b = 1 (Figure 13a left), and a probability of exceeding M2.5 at 3% per small square (Figure 13b left). The a/b-values and the probability of exceeding M2.5 are shown in the right-hand side figure for each of the arbitrary spatial filter volumes. The highlighted subarea consists of 42 small areas and therefore has N@0 = 420. The TGR (red line), a/b, fmax (coloured distribution) for a small square and the highlighted area are shown in Figure 15.

Evaluating the seismic hazard for arbitrary volumes leads to the amplification of the hazard for larger volumes and a misrepresentation of the hazard. Figure 14 illustrates the same effect in a different scenario where the event rate is not uniformly distributed throughout the whole mining area. Figure 14 illustrates the fact that arbitrarily chosen filter volumes can mask the true spatial distribution of seismic hazard. These comparative hazard maps can be corrected by normalizing the assessment with respect to the volume.

The Journal of the Southern African Institute of Mining and Metallurgy

Addressing misconceptions regarding seismic hazard assessment in mines

Figure 14—The influence of volume size on the evaluation of hazard (a) a/b distribution and (b) probability of exceeding M2.5, MUL = 4, mmin = 0

a) Small square

b) Highlighted area

Figure 15—The TGR (red line), a/b, and fmax (coloured distribution) for the (a) small squares, and (b) the highlighted area in Figure 13

Hazard normalization Spatial normalization For the case illustrated in Figure 13, the statement was made that the probability of exceeding M2.5 per small square is 3%. In this statement, the hazard is normalized with respect to volume. This normalized value is essential for obtaining a spatial distribution of hazard or a true comparison of hazard between different volumes but it does not provide the absolute hazard. The hazard for the whole mining area is not 3%. For the whole mining area, we need to integrate the hazard of each of the sub-areas to obtain 95%. In other words, there is about a 95% chance of experiencing M2.5 anywhere in the mine, but it is equally likely to occur The Journal of the Southern African Institute of Mining and Metallurgy

anywhere in the mine with a 3% probability of occurring in any one of the small squares.

The total hazard for the whole mine and the spatial distribution of that hazard are independent of the size of the small squares, but the actual value associated with the small square is dependent on the size of the squares. To represent the spatial distribution in a way that is independent of the sub-square size, one would need to define a characteristic volume to which all values are normalized. It should be noted that the size of a characteristic volume does not influence the hazard calculations nor the spatial hazard distribution, but only influences the actual numbers by which the hazard is expressed.

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Figure 16—Hazard rating based equivalent representative volume, independent of the sub-square size

of a sphere with a radius of 50 m. The a/b-values in the scenario shown in Figure 14 are spatially normalized to a characteristic volume shown in Figure 16a.

The sub-square size of 20 m was assumed, and since the example is a two-dimensional one, an equivalent representative area with radius 50 m was used. The comparison between the non-uniform sub-volume scenarios in Figure 14a and Figure 16a shows that the normalization to a characteristic volume enables a more reasonable comparison of seismic hazard distribution.

Spatial normalization, however, does not correct for the loss of information that occurs when the spatial filters do not take account of the underlying seismic sources, as only a mean value for each sub-volume is retained. As a result, some masking of seismic hazard trends still occur. The left-hand side diagram of Figure 16a illustrates the fact that when the evaluation volume is small enough to capture the change in seismic sources in space, the normalized a/b values provide a useful hazard rating for quantifying the spatial distribution of the seismic hazard.

Wesseloo (2018) takes this one step further and defines a hazard rating based on the same definition for the characteristic volume. The hazard rating is defined as the magnitude with a 15% probability of exceedance within the equivalent representative volume. This hazard rating definition was chosen to produces similar rating values to the hazard scale originally proposed by Hudyma and Potvin (2004), with which many mines in Australia and Canada are familiar. For the scenario in Figure 14b, this leads to the spatial hazard rating shown in Figure 16b.

This approach provides a method for representing the spatial distribution of hazard which is independent of the size of the sub-volume.

Time normalization The normalization of hazard is as important in the time domain

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as it is in space. To enable direct comparison between hazards of different durations, it is necessary to normalize the calculated probability to the same equivalent timescale. This normalization can be performed as follows (Wesseloo, 2018): [17] where Tn, Te are the normalized timeframe and the original timeframe, respectively PTn, PTe are the equivalent probabilities expressed for timeframes Tn and Te, respectively A hazard with a weekly probability of 1% can be expressed with equivalent annualized values as 1 − (1 − 0.01)52 = 40%, while a hazard with a biennial probability of 50% can be expressed in equivalent annualized values as 1 − (1 − 0.5)0.5 = 30%.

Normalization can be performed to any timescale but short time periods should be avoided as this leads to small numbers which are often misinterpreted as small hazards. The use of one year (annualization) seems a reasonable approach and corresponds with the practice in other branches of engineering, financial risk management and corporate governance (Jonkman, van Gelder, and Vrijling, 2003; Terbrugge et al., 2006; Stacey, Terbrugge, and Wesseloo, 2007; Wesseloo and Read, 2009).

Annualization also allows one to calculate the associated risk and evaluate it against corporate accepted annualized risk levels.

Annualization of hazard values leads to an ‘annual probability’, but it is important to note that this should not be interpreted as the probability for a physical year (future or historical). It is the probability value appropriate to the timescale for which the mean seismic rate is applicable, expressed in equivalent annualized terms.

The Journal of the Southern African Institute of Mining and Metallurgy

Addressing misconceptions regarding seismic hazard assessment in mines The example calculations following Equation [17] assume that both hazards are present over a long time period. If, for example, a hazard with a weekly probability of 1% is present only for one week in the year, the yearly hazard would also be 1%. This sometimes leads to misunderstanding in the application of hazard assessments in industry where a short-lived but repeating hazard is sometimes evaluated in isolation.

Seismic hazard in a mine is transient in space and time and, although the seismic hazard at a specific location might be short-lived, the hazard is of a repeating nature. For example, the hazard associated with the mining of a single stope might be present only during the time it takes to mine that stope, but, a similar hazard might occur due to the mining of the next stope. Both long-term and repeating hazards can be normalized temporally using Equation [17].

Wesseloo (2018) illustrated this concept with the following fictitious mining scenario that consists of 12 stopes (Figure 17). Each of the stopes is mined for a month, during which a seismic response is induced in the indicated area around it. The seismicity in this surrounding area ceases when mining in this stope is complete. During the following month, the next stope is mined with the associated induced seismicity limited to its surrounding area. The argument can be further simplified by assuming a constant b = 1 over the whole volume and the whole year, and by assuming that the total number of events occurring in the surrounding area of each of the stopes is the same at N@-2 = 1000 (a = 1).

For each stope, the probability of exceeding M2 is 3.92%, and if evaluated in isolation, may be regarded as acceptable.

Cumulating the number of events for all 12 stopes, however, results in the total probability of exceeding M2 of 38%. In the year, the company is exposed to the total aggregated hazard of P[M > 2] = 38%, even though each stope only has an individual monthly hazard of probability 3.9%. If exposing the company to the yearly hazard of 38% is not acceptable, by implication, it is not acceptable to expose the company to the hazard associated with every one of those stopes individually.

Conclusing remarks The topic of seismic hazard assessment is subject to several misconceptions in the mining industry, and this paper addressed some of these. The term ‘Mmax’ is used for several different concepts which appear to contribute to these misconceptions. To avoid confusion, I suggest that the term ‘Mmax’ should be reserved for the concept of the maximum credible event. Other concepts referred to as ‘Mmax’ should be referred to by unique names. I propose the use of ‘Xmax’ or ‘Mobs’ for the largest recorded event in a data-set, ‘a/b’ for the value of the fitted GR relationship

at N = 1, and, ‘MUL’ for the upper truncation value of the FM distribution.

The value of the fitted GR relationship at N = 1 (a/b) is sometimes interpreted as the expected largest event. This value is, however, the mode of the distribution of the largest expected magnitude with a 63% chance of being exceeded for the openended GR relationship. The probability of exceedance is smaller for truncated GR relationship and depends on the upper limit of the distribution.

The accuracy of methods for estimating Mmax is low when a/b << Mmax and tends to underestimate Mmax. Reliable results can only be obtained when Mmax is based on large data-sets, (e.g.

N@0 > 1000 for Mmax = 2.5 N@M > 10 000 for Mmax = 4.5).

When assessing the probability of exceeding a specified magnitude P[M > Mt], underestimation of MUL (MUL = Mmax - δ) leads to larger errors than overestimation of MUL by the same amount (MUL = Mmax + δ). Underestimating MUL is always optimistic, while overestimation s always conservative. For the purpose of hazard assessment, it is therefore prudent to use values for MUL that are deliberately conservative.

Artificial Black Swan events can be created when MUL underestimates Mmax. This can occur simply due to the effect of uncertainty. The under-recording of low frequencies due to sensor limitations leads to the underestimation of Mmax and also leads to artificial Black Swan events. To combat this problem, sensors which are able to adequately record the lower frequency content of the event sizes expected at the mine, need to be included in the system. In lieu of this, corrections may be applied to the recorded values to compensate for the effect of the sensors underrecording.

Assessment of the seismic hazard needs to be performed on sub-volumes for which the b-value and the event rate can be assumed to be constant.

For comparative hazard evaluation in space and time, both spatial and temporal normalization are necessary. Normalization to a characteristic volume equal to a sphere with a 50 m radius is suggested. Annualization of hazards is proposed for temporal normalization.

Acknowledgement

I thank William Joughin and Lindsay Linzer for their valuable comments on the manuscript. I also thank all my colleagues at the ACG for their support and, in particular, Yves Potvin, Gerhard Morkel, Kyle Woodward, Dan Cumming-Potvin and Stuart Tierney for fruitful technical discussions. I also would like to thank Paul Harris and Matt Heinsen Egan for always being available to help me with any scripting troubleshooting and app building in mXrap. Thanks also to Josephine Ruddle and Christine Neskudla

Figure 17—Conceptual mine layout for an illustrative example, consisting of 12 stopes with a surrounding area of seismicity induced by the stoping activity (Wesseloo, 2018) The Journal of the Southern African Institute of Mining and Metallurgy

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Addressing misconceptions regarding seismic hazard assessment in mines for proofing my language. The content of this paper has flowed from work done over the span of several years with the support of the mXrap Consortium.

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Appendix A The probability of exceeding an event size of a/b within n events For the TGR relationship, the CDF of the event distribution from the magnitude of completeness mmin is then given by [A.1] The probability of exceeding an event size of M within n events is given by (Gibowicz and Kijko, 1994): [A.2] where: Fmax(M) is the cumulative distribution function of the magnitude of the largest event F(M) is the cumulative probability density function describing the magnitude distribution of event size.

The probability of exceeding the value of a/b within n events of magnitude ≥ mmin is given by [A.3] And defining MUL = k·a/b leads to [A.4] This relationship is independent of mmin and can be written in terms of the a-value as [A.5] which, for the open-ended GR relationship reduces to [A.6]

The mode of the distribution of Fmax The mode of fmax (x) can be obtained as follows:

[A.7]

which reduces to x = a/b for both the open-ended and TGR relationship. u The Journal of the Southern African Institute of Mining and Metallurgy

Do stopes contribute to the seismic source?

L.M. Linzer1, M.W. Hildyard2, S.M. Spottiswoode3 and J. Wesseloo4

Affiliation: 1 SRK Consulting (South Africa) and University of the Witwatersrand, South Africa.

2 University of Leeds, United Kingdom.

3 Independent Consultant, South Africa.

4 Australian Centre for Geomechanics, University of Western Australia.

Correspondence to: L. M. Linzer

Email:

LLinzer@srk.co.za

Dates:

Received: 30 Oct. 2019 Revised: 9 Dec. 2019 Accepted: 22 Jan. 2020 Published: January 2020

How to cite:

Linzer, L.M., Hildyard, M.W., Spottiswoode, S.M., and Wesseloo, J. Do stopes contribute to the seismic source?.

The Southern African Insitute of Mining and Metallurgy DOI ID: http://dx.doi.org/10.17159/24119717/954/2020

This paper was first presented at the Deep Mining 2019 Conference, 24â&#x20AC;&#x201C;25 June 2019 Misty Hills Conference Centre, Muldersdrift, Johannesburg, South Africa.

Synopsis Parameters such as source location, seismic moment, energy, source size, and stress drop are routinely calculated from mining-induced seismic data. Seismic moment tensors are inverted less routinely because their calculation is more complex and their accuracy depends on the network geometry, among a number of other factors. The models utilized in the source parameter calculations, the most wellknown of which is the Brune model, were developed for the global seismicity problem and assume a solid, homogeneous Earth model. However, the tabular orebodies in South African gold and platinum mines are mined extensively and the excavations (stopes) can extend for many kilometres. The seismic source mechanisms on deep-level gold mines are generally compatible with shear failure (Hoffmann et al., 2013), whereas the source mechanisms of events at intermediate-level bord and pillar mines in the platinum district are more compatible with pillar failure and accompanying stope closure (Spottiswoode, Scheepers, and Ledwaba, 2006; Malovichko, van Aswegen, and Clark, 2012).

In this paper we investigate the influence of the stope on seismic inversions for the scalar moment, corner frequency/source radius, stress drop through numerical modelling using WAVE3D. The main objective is to determine whether the source parameters calculated from the recorded waveforms are due to a combination of the stope and shearing sources, rather than being related only to a shear source in the host rock. The modelled source is shear rupture in the footwall of a stope. The results show that the stope appears to have an appreciable effect on the seismic inversions. The seismic moment and source radius of the shear source in the stope are larger for the model with a stope compared to the model with no stope. The stress drop for the case with a stope is less than the applied stress drop, which could be an effect of the apparently larger source. This work provides a possible explanation of the second corner frequency often observed in the spectra of seismograms recorded in South Africa platinum mines.

This has implications for the accurate determination of source parameters and the assessment of the intensity of shaking in stopes.

Keywords mine seismicity, source mechanisms, numerical model, seismic inversion.

Introduction It is widely accepted that the majority of mining-induced seismic events are shear-type events. As a result, the classical shear-slip model has been used to explain mining-induced seismicity worldwide since 1975 (Spottiswoode and McGarr, 1975). This model was originally developed for global seismic analysis and considers a planar fault surface within a solid Earth, over which instantaneous slip occurs.

However, since the tabular gold and platinum deposits in South Africa are mined extensively and the excavations can extend for many kilometres, the solid Earth assumption is violated. In addition, the free surfaces created by mining enable volume changes to occur, such as the crushing or dilation of a pillar, thus violating the shear-slip approximation. A further implication of having free surfaces underground is that if a seismic event occurs near the excavation, it is possible that the stopes themselves are an integral part of the seismic source. If the event is within one source dimension, then the timing of stope convergence and ride will overlap in time with the source, affecting both the shear slip and recordings in the far field.

Previous work done by Donovan et al. (2006) attempted to investigate the effects of stopes on the seismic wavefield generated by the violent failure of a typical crush/yield pillar. Forward modelling techniques were applied using a finite-difference code called WAVE (Cundall, 1992; Hildyard, Daehnke, and Condall, 1995; Hildyard 2001; Hildyard and Young 2002), which has the unique ability to model

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Do stopes contribute to the seismic source? the effect of voids (or stopes) on the seismic wavefield in three dimensions. The wave propagation can be followed from its initiation at a source through its reflection and refraction from structures in the stope vicinity.

The effects of excavations on seismic data This section consolidates the findings of research studies that investigated the effects of excavations on seismic waves. It is subdivided into studies based on recorded data from specialized underground networks and research where numerical modelling was applied.

Observed effects from recorded data Attenuation effects Waves travelling through the fracture zone enveloping excavations suffer greater attenuation of the high frequencies than waves passing through solid rock. Cichowicz (2001) estimated that the quality factor of the medium, Q, was approximately 40 in the fracture zone around a stope, whereas Q values of around 400 are typical in unfractured rock away from a stope.

Churcher (1990) investigated this path effect further by comparing the signals that travelled through the solid rock mass with signals from the same event that travelled through the fracture zone. Since the corner frequency is a parameter that is controlled by the source, it should be independent of recording site position. However, it was found that the corner frequency values calculated from waveforms that propagated mainly through unfractured rock were 1.3 to 3.4 times higher than the values computed from ray paths through fractured ground. Since the source radius and stress drop are source parameters that depend on corner frequency, the effect of having reduced corner frequencies is that the source radius will appear to be too large, and the stress drop too low.

Amplification effects Milev et al. (2002) recorded peak particle velocities (PPVs) on the surface of the excavation using instruments such as the peak velocity detector (PVD) and ground motion monitor (GMM), and compared them with PPVs recorded by the in-mine system. The geophones of the in-mine system were installed in boreholes at depths of approximately 10 m in solid rock. The PPVs recorded in solid rock were corrected for distance to compensate for geometrical spreading. Common events (events that triggered both the PVD and in-mine network) were then compared. The researchers found that the PPVs recorded on the surface of the excavation were amplified 5–6 times. Since an amplification of two times can be explained by constructive interference from reflections off the free surface, other explanations must be sought. One reason could be the propagation of surface waves along the free surface of the excavation.

Possible stope effects It is instructive to list the characteristics of seismograms generated by shear failure before discussing those associated with non-shearing sources. Spottiswoode, Scheepers, and Ledwaba (2006) summarize these characteristics as follows. ➤ The dominant frequency of P-waves is noticeably higher than that of S-waves. ➤ The source dimensions calculated using the Brune (1970) model give realistic estimates of the size of earthquakes.

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➤ The P-wave displacement pulses for ray paths through solid rock are clear and unidirectional. ➤ The displacement spectrum is flat for frequencies lower than the corner frequency. At frequencies above the corner frequency, the displacement spectrum falls off with frequency (f) according to f-2 until anelastic attenuation absorbs energy at high frequencies (Spottiswoode, 1993). ➤ The seismic moments estimated from P- and S-wave pulses are approximately equal. ➤ The seismic moment M0 estimated from spectra provides an accurate ’volume of ride’ for shear sources (M0 = G ∫ R.dA = GV where G is the shear modulus, and the integral can be seen to represent a volume of ride V = RA, where R is the mean ride (m) and A is the area of rupture surface (m2) (Ryder, 1988). ➤ Apparent stress (σA = GE/M0, where G is the shear modulus, E is energy) is used as an estimate of stress change at the source. Note that magnitude based on radiated energy (i.e. using log10 (E) = 1.5M + 4.8 where E is in joules (Gutenberg and Richter, 1956), and magnitude based on seismic moment (log10 (M0) = 1.5M + 9.1 where M0 is in Nm (Hanks and Kanamori, 1979) are identical for σA = 2 MPa and for G = 40 GPa. Values of σA are typically about 0.1 MPa for gold mine events (van Aswegen and Butler, 1993). The apparent stress varies from 0.1–10 MPa over the entire range of earthquake sizes (17 orders of M0) (Ide and Beroza, 2001).

Spottiswoode, Scheepers, and Ledwaba (2006) propose a failure model which consists of pillar crushing driven by stope closure. This model explains several characteristics of the data that cannot be explained by the classic shear-slip model: ➤ The corner frequency was an order of magnitude lower than that expected from failure of the pillar. ➤ The P-wave source pulses were bi-directional. ➤ The displacement spectrum falls off with frequency (f) according to f-3 at frequencies above about 100 Hz. ➤ The P-wave moments were consistently higher than those estimated from the S-waves. ➤ Extremely low apparent stress of about 0.03 MPa (Spottiswoode, Scheepers, and Ledwaba, 2006).

In addition, Gibowicz (1990) notes that whenever the size and geometry of underground damage caused by rockbursts could be estimated in Polish mines, the observed source radius was considerably smaller than that predicted by the Brune model.

Since the source radius r0 is proportional to the inverse of the corner frequency f0, Gibowicz’s observation that the calculated source radii are too large implies that the measured corner frequencies are too low.

Modelled effects from observations of simulated data Effect of high-angle fractures Daehnke (1997) studied the effects of seismic waves on highangle fractures in the vicinity of stopes by means of dynamic photoelastic experiments. Photographs of the resulting fringe patterns were analysed theoretically and back-analysed using the WAVE code to validate the accuracy of the elastodynamic models.

The physical models consisted of a slot-shaped opening representing the stope, positioned in the centre of a thin composite plate made from photoelastic materials. The wave patterns emanating from a blast source in the bulk material The Journal of the Southern African Institute of Mining and Metallurgy

Do stopes contribute to the seismic source? were photographed using a high-speed camera. Figure 1 shows a comparison between the experimental and the WAVE results at a snapshot of 169 µs. It was concluded that WAVE accurately modelled the diffraction, refraction, transmission, and reflection of stress waves in these models. Similar experiments in photoelastic material have demonstrated that modelled wave behaviour can closely correspond to that observed (Daehnke, Rossmanith, and Knasmillner, 1996; Daehnke and Hildyard, 1997; Uenishi, 1997).

These experiments suffer from the deficiency of being two-dimensional analyses, and no direct detailed comparisons between measured and modelled seismograms were made (Hildyard, 2007).

Wang and Cai (2015) investigated the effect of the wavelength to excavation span (λ/D) ratio on ground motion and PPVs using SPECFEM2D. They concluded that the λ/D ratio has a significant influence on the ground motions and PPV distribution surrounding underground openings. The study showed that higher PPVs were observed when the λ/D ratio was decreased, suggesting larger span excavations may be at increased risk of dynamic failure. When λ/D > 30, the loading is considered quasistatic, whereas for λ/D < 20 significant interaction of seismic waves takes place and the loading must be considered dynamic.

For the majority of mine excavations influenced by fault slip events, λ/D < 10 (Wang and Cai, 2015).

Effect of mine layout on seismic wave propagation Forward modelling studies in which the effects of an excavation on wave propagation were examined are described in Hildyard, Napier, and Young (2001) and Hildyard (2007). One study used a tabular stoping geometry typical of longwall mining in deeplevel mines in South Africa. The mining layout consisted of an excavation with a stoping width of 1.5 m and extending laterally for hundreds of metres. Wave propagation was simulated for a vertical slip event in the footwall of the pillar, parallel to the direction of mining advance and just behind the face position of the mining. The event had a moment of 1.2 × 105 MNm, and a moment magnitude of 1.3. The rupture was propagated at 3000 m/s with a uniform stress drop of 9.35 MPa. The stress drop at any point occurred over 2 m, and the whole event over 6 m.

In Figure 2, the maximum vertical velocity in a plane 2 m below the stope is compared with the case if the excavation did not exist. It was found that the influence of the stope on the wave propagation is significant, affecting the amplitude and distribution of PPV. The PPVs in the model with a stope are several times those at similar distances in the solid model. The effect on the induced tensile stresses is even more marked, where the value 200 m from the event is increased from 0.06 MPa in the solid model to 1.6 MPa due to the influence of the excavation (see Figure 3).

Velocities at the far pillar are up to six times those in the solid model without the excavation. Maximum amplitudes follow the face outline, and the maximum amplitudes deep into the stope are around four times those in the solid model without the excavation. Hildyard (2007) concluded that a relatively small event (magnitude of 1.3) occurring near the edge of a stope can lead to these amplifications and induce tensile stresses 200 m from the event. This effect is caused by surface waves propagating along the free surface of the excavation.

Figure 2—Plan sections through two three-dimensional elastic models.

Model (a) shows a tabular mining excavation, while model (b) is a purely solid material. Contours indicate the maximum vertical velocity (in m/s) induced in a plane (from Hildyard, 2007)

Figure 3—Maximum induced tensile stress (in MPa) for σzz. Plan sections are shown for two distances below the excavation, and indicate that horizontal tensile stress of up to 2 MPa is induced close to the surface, and that this falls off rapidly with distance from the surface (from Hildyard, 2007)

Figure 1—The effect of high-angle fractures (a) physical model (b) numerical mode (from Daehnke, 1997)

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The influence of the λ/D ratio on PPV amplification was also investigated by Raffaldi, Johnson, and Chambers (2017) using UDEC. The graph in Figure 4 shows a clear relationship between PPV amplification and source frequency, with maximum amplification occurring at a frequency of 150 Hz, which represents a λ/D ratio of 3. Note that these UDEC models were two-dimensional.

McKenzie (2017) also explored the effect of λ/D ratio on PPV amplification using WAVE3D. The key difference between this study and those by Wang and Cai (2015) and Raffaldi, Johnson, and Chambers (2017) is that it incorporates the third spatial dimension. The relationship between source frequency and the PPV amplification factor is consistent with that shown

by Raffaldi, Johnson, and Chambers, (2017). However, the equivalent modelling in 3D for two sets of tunnels with different dimensions (4 × 4 m and 4 × 8 m) did not show a clear relationship between the λ/D ratio and PPV amplification.

The testing does show clearly that source frequency controls the extent of PPV amplification, but there is not a correlation between the λ/D ratio and the amplification factor between the smaller tunnels and any of the three orientations modelled for the larger tunnels. McKenzie (2017) concludes that although it was not possible to establish a clear relationship between the λ/D ratio and PPV amplification in 3D, as was achieved by Raffaldim Johnson, and Chambers (2017), the discrepancy is possibly the result of modelling a specific source mechanism in WAVE3D. Raffaldi, Johnson, and Chambers (2017) assume a plane wave interacting with a tunnel in 2D, whereas the McKenzie (2017) study models wave fronts striking in a variety of oblique orientations relative to the different excavations.

Effect of the fracture zone on seismic wave propagation Linkov and Durrheim (1998) propose that amplification is due to the additional energy released as waves pass through the fracture zone, which is in a post-failure, strain-softening state. In a further study discussed in Hildyard, Napier, and Young (2001), the effect of fractures on the near-field and far-field PPVs was evaluated for stope-normal (vertical) and stope-parallel (horizontal) fractures. In addition to the fracture zone causing waves to attenuate due to scattering of high frequencies (see Churcher, 1990), Hildyard (2001) found that the effective elastic modulus of rock may be reduced, which could increase amplitudes for long wavelengths, leading to velocity amplifications. This study showed that the amplitudes in both the near and far field were influenced in a complex fashion by: stress drop, sense of slip, excavation surface, excavation outline, and the proximity of any part of the source (rather than the source centre) to the excavation. The stope free surface caused the near-field PPV to increase by 40%, and the far-field PPVs

increased by 600%. The effect of the fracturing was to increase both near- and far-field stope velocities by up to 50%. Hildyard (2007) concluded that the damage potential from an event near an excavation cannot be readily inferred from aspects such as moment, magnitude, and the proximity to the source centre, as this ignores the effect of free surfaces and fracturing.

Durrheim (2012) suggests that the amplification of PPVs in the fracture zone could be due to velocity contrasts, trapping seismic energy from seismic waves and enhancing surface wave formation (e.g. Love and Raleigh waves). A recent study by Zhang, Swan, and Nordlund (2015) investigated the effect of fracturing on velocity amplification through universal distinct element code (UDEC) modelling. The model consisted of a one-dimensional elastic rock bar with a length of 300 m and a width of 1 m. A dynamic load was applied normal to the surface of the bar and propagated through a series of regularly spaced parallel fractures. Their results showed that multiple near-surface fractures amplify PPVs, with recorded increases of 2–3.6 times above input velocities. The dominant influencing factors were ascribed to wave frequency, fracture zone thickness, fracture spacing, and fracture stiffness. These authors suggest that the primary mechanism through which fractures caused amplification was the interaction of fractures with seismic body waves.

Effect of the stope on seismic parameter inversions In a separate study that applied forward modelling techniques (Linzer and Hildyard, 2005), the effect of stopes on the accuracy of seismic inversions for source parameters was analysed.

WAVE was used to model seismic wave propagation around a faulting source which daylights into a stope. The main objective was to determine whether the source parameters calculated from recordings (seismograms) of the radiated wavefield were influenced by the presence of the stope. Since the source mechanism is known in detail (because the source is explicitly input into the model) the accuracy of the seismic inversions could be evaluated.

The results showed significant influences due to the stope.

The stope appeared to have an appreciable effect on the seismic inversion for the scalar moment since the moment computed from the seismograms was almost double the moment of the fault source, indicating that the stope could be behaving like a source and contributing to the overall wavefield. In addition, the source area calculated from the seismograms was significantly larger than the fault source area of the model. However, the conclusions of Linzer and Hildyard (2005) admitted some uncertainty, particularly as to whether the seismograms were recorded at sufficiently far distances in comparison to the source size and typical field measurements.

This paper builds on an unpublished study and takes advantage of the advances in computing capabilities. Models that previously had to be run on a cluster can now be run on a highend desktop workstation.

WAVE3D modelling

Figure 4—PPV amplification factor versus source frequency applying a PPV input of 0.25 m/s (from Raffaldi, Johnson, and Chambers, 2017)

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Two models were run in WAVE3D, each having a shearing source.

The control case consisted of just the shearing source to eliminate the stope effect, whereas the second model included a stope.

Each model comprised an orthogonal, equispaced grid of 720 × 720 × 720 elements (a total of 374 million grid points) with grid spacing of 1 m. These models each required 32 GB of RAM and took 16 hours to complete when run on a high-end workstation.

In 2006, similarly sized models had to be run on a cluster and ran over five days.

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Do stopes contribute to the seismic source?

The shearing source consisted of a 20 m × 20 m vertical fault positioned below and in the centre of the 60 × 60 m stope.

The fault plane daylighted into the stope (i.e. the slip plane was exposed in the stope). The slip plane was explicitly included in the model. The rupture was initiated at a central point at the top of the slip plane and propagated downwards along the plane at a rate of 90% of the S-wave velocity. The P-wave velocity used was 6610 m/s and the S-wave velocity was 3865 m/s. The source geometry for the shearing source is shown in Figure 5. Twentyfour triaxial geophones with axes oriented parallel to the x-, y-, and z-axes of the model were placed around the source centre at a distance of 320 m from the source so as to provide complete coverage of the focal sphere in three dimensions.

The hypocentral distance for all geophones was 320 m, 32 times the source radius of the slip plane. Consequently, the recordings related to slip of the fault plane should be in the far field and the near-field terms should be negligibly small. If coseismic convergence of the 60 m2 × 60 m2 stope occurs when the fault slips, the stope could radiate elastic waves contributing to the total wavefield. The hypocentral distance of 320 m is just over 10 times the source radius of the stope, and therefore the near-field contribution of the stope should also be negligible.

parameters (moment, magnitude, source radius, and stress drop) were also computed from the windowed P- and S-wave phases.

The source parameter results are given in Table I. Snapshots for the two models consisting of contoured values of PPV in the x-y plane (vertical cross-section through the source centre) for six discrete time intervals (from 3.4 ms to 67.32 ms after event initiation) are shown in Figure 6 and Figure 7. To enable comparison between the two models, the same colour scale is used for all cases, where red represents PPV values of 0.5 m/s and above. The maximum PPV for each time-step is also indicated on the figures. The effect of the propagating source is to compress the wavefield below the source, causing a Doppler shift to the higher frequencies. The presence of the stope disturbs the radiation pattern generated by the shear source and prevents wave propagation through the stope while causing the wavefield to diffract around the edges of the stope. The PPVs generated by the shear source below the stope are significantly higher than those for the case with no stope, reaching maxima of 3.1 m/s (stope case) and 1.4 m/s (solid case) some 6.8 μs after source initiation. Not only are the PPVs higher for the stope case (see Figure 8), but the areas having PPVs > 0.5 m/s are significantly larger.

Examples of the synthetic seismograms recorded by site 1 (see Figure 5) of the 24 geophone sites, for the two models are given in Figure 9 and Figure 10. The red, blue, and green traces are the seismograms recorded by three channels of a triaxial geophone, where the sensors are oriented orthogonal to one another (red: channel along x-axis; blue: channel along y-axis, and green: channel along z-axis). In all the figures showing seismograms the waveforms are rotated towards the source to maximize the P-wave in the radial direction (while minimizing the S-waves) and maximize the S-waves in the transverse directions (while minimizing the P-wave). Velocity seismograms are given, as well as the integrated velocity traces (i.e. displacements).

The P-S wave separation is excellent for all the traces, enabling the onset of the wave phases to be picked. The traces generated by the shear sources (in a solid, and with a stope) show well-developed S-waves, indicative of a source dominated by shearing. This is consistent with the P-S moment ratios of approximately 0.7 given in Table I. The velocity and displacement traces for the shear source in the solid are nearly perfect textbook examples of signals generated by a shear source (see Figure 9). There is a small amount of overshoot (e.g., blue displacement trace), but it is not clear what the cause is. Comparison of the integrated

Results and discussion The synthetic seismograms recorded by the spherically arranged geophones were converted into PRISM format and imported into AURA, the seismogram processing tool written by CSIR. An AURA database was set up using the network geometry specified in the WAVE3D job files, and the converted seismograms were imported into AURA. The P- and S-wave phases of the synthetic seismograms were picked and the events located. Source

Figure 5—Model geometry for the shearing source daylighting into a stope.

Geophone positions shown by triangles

Table I

Source parameters calculated from synthetic seismogrammes WAVE job

X (m)

Y (m)

Z (m)

Err. (m)

Mag.

MoP (109N.m)

MoS (109N.m)

Mo (109N.m)

MoP/MoS

Shear source, no stope Shear source with stope

361.0 360.8

369.1 361.6

361.3 361.3

1.2 4.3

1.4 1.7

296 534

446 715

396 655

0.7 0.7

EP (MJ)

ES (MJ)

Energy (MJ)

foP (Hz)

foS (Hz)

fo (Hz)

rP (m)

rS (m)

r (m)

Δσ (MPa)

2.7 4.7

82.0 253.0

84.7 258.0

107.0 80.2

87.2 86.7

97.1 83.5

23.0 30.7

16.5 16.6

20.6 25.8

19.8 16.7

WAVE job Shear source, no stope Shear source with stope

X,Y,Z = Event location; Error = Location error; Mag. = Moment magnitude; MoP, MoS = Moment calculated from P- and S-windows of modelled velocity seismograms; EP, ES = Energy calculated from P- and S-windows of modelled velocity seismograms; foP, foS = Corner frequencies calculated from the velocity spectra from the P- and S-wave windows; fo = Corner

frequency calculated from the velocity spectra (weighted average of foP and foS); rP, rS = Source radii calculated from the P- and S-wave windows; r = Source radius (weighted average of rP and rS); Δσ = Stress drop

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Figure 6â&#x20AC;&#x201D;Snapshots (vertical cross-sections) for time-steps 2, 4, 6, and 8 ms comparing the PPV generated by a shear source in a solid (left) and in a solid with a stope (right). Linear scale (red, max. = 0.5 m/s)

displacements recorded at site 1 (see Figure 5) for the shear model with a stope with that in the solid shows that longer wavelengths are present in the wavefield, evidently a result of the presence of the stope.

It is instructive to compare signals recorded above and below the stope plane for the shearing source. Site 11 is located in the hangingwall above the stope plane, and site 5 is in the footwall below the stope plane (see Figure 10). Velocity and integrated displacement traces recorded above and below the stope plane for a shear source in a solid, and in the presence of a stope, are shown in Figure 11 and Figure 12. The stope significantly increases the complexity of the velocity and displacement traces recorded above the plane of the stope. The recordings made in the footwall, below the plane of the stope, show slightly less complexity, probably because the shear source is also in the

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footwall. However, the waveforms differ significantly from those for the model of the shear source in the solid.

The velocity spectra for sites above and below the stope are shown in Figure 13. The spectra are compared with the modelled cases where no stope is present. The stope has an obvious effect and causes low-frequency components to be added to all the traces in the range 1 Hz to 200 Hz, with a peak at about 100 Hz. The modelled source of shear failure in a pillar where the fault daylights into the stope is likely to be a worst-case scenario.

Preliminary investigation shows that with non-daylighting faults the waveforms are affected in a similar way and differ considerably from the waveforms without a stope present. In the case where the fault does not intersect the stope the stope convergence is less, resulting in a lower stope contribution to the wavefield.

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Figure 7—Snapshots (vertical cross-sections) for time-steps 10, 12, 14, and 16 ms comparing the PPV generated by a shear source in a solid (left) and in a solid with a stope (right). Linear scale (red, max. = 0.5 m/s)

It is expected that the properties of the stope will influence the overall behaviour of the system. Preliminary investigations, however, show that the effect of backfill is very limited and noticeable only with a very high stiffness. The effect of in-stope pillars has not been modelled as part of this investigation. Due to the high stiffness of the pillars it is expected that their influence will be more considerable. It is also expected that the presence of in-stope pillars would limit the span, which limits the lower frequency contribution in favour of higher frequencies.

Future work will attempt to model more realistic scenarios by including a fracture zone around the stope, in-stope pillars, an actual bord and pillar geometry, and a comparison of synthetic waveforms with recorded seismograms.

The Journal of the Southern African Institute of Mining and Metallurgy

Summary of observations The main observations can be summarized as follows. ➤ The PPVs generated by the shear source below a stope are significantly higher than those for the case with no stope, reaching maxima of 3.1 m/s (stope case) and 1.4 m/s (solid case) some 6.8 m/s after source initiation. ➤ The shear sources in the presence of a stope show that longer wavelengths are present in the wavefield, evidently a result of the presence of the stope. ➤ The stope increases the complexity of the traces. ➤ The stope has an obvious effect and causes low-frequency components to be added to all the traces, in the range 1–200 Hz, with a peak at about 100 Hz. VOLUME 120

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Figure 8—PPVs generated by shear sources, for the solid case and the case with a stope

Figure 10—Position of geophone sites selected above and below stope plane

Figure 9—Velocity and displacement traces recorded at site 1 (see Figure 5) for a magnitude 1.4 shear source in a solid and with a stope (red: channel along x-axis; blue: channel along y-axis; green: channel along z-axis)

Figure 11—Site 11. Shear source. Velocity (top) and integrated displacement (bottom) recorded above stope plane for source in solid (left) and source with stope (right). Red: channel along x-axis; blue: channel along y-axis; green: channel along z-axis

Figure 12—Site 5. Shear source. Velocity (top) and integrated displacement (bottom) recorded above stope plane for source in solid (left) and source with stope (right). Red: channel along x-axis; blue: channel along y-axis; green: channel along z-axis

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(a) x-component

(d) x-component

(b)y-component

(e) y-component

(c) z-component

(f) z-component

Figure 13—Shear source. Velocity spectra calculated from entire seismogram for sites above and below the stope plane. Red: shear source with stope black: shear source with no stope

The following observations refer to the source parameters listed in Table I. ➤ The magnitudes (calculated from the integrated square of the energy) for the shear model in the presence of a stope are larger than those computed from the solid models. It therefore appears that the stope is adding to the overall size of the event.

The Journal of the Southern African Institute of Mining and Metallurgy

➤ The moments calculated from the spectra mirror the magnitude trends. The moment of the fault source explicitly included in the model is 292 × 109 N.m and that of the stope is 382 × 109 N.m. Adding the two moments together give a total moment of 674 × 109 N.m. For the shear source with no stope, the moment computed from spectra of the synthetic seismograms is 396 × 109 N.m for the case in the VOLUME 120

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Do stopes contribute to the seismic source? solid, and 655 × 109 N.m for the case with the stope. Since the moments computed from the seismograms are all larger than the fault moment calculated directly from the model, the stope is clearly contributing to the moment. ➤ The shear source models had an area of 400 m2 for the fault, and 3600 m2 for the stope. The source areas computed from the source radii, which are computed from the spectra, are 1333 m2 for the shear source in the solid and 2091 m2 for the shear source with the stope. ➤ The actual stress drop applied in the model was 50 MPa. The stress drops computed from the source radii for the shear sources in a solid and near the stope, are lower: 20 MPa and 17 MPa, respectively. This could be the effect of the apparently larger source, i.e. the stress of the model is applied only over the fault source area, but since the stope is part of the effective source, the average stress drop is much lower than 50 MPa.

Conclusions

The programme WAVE3D was used to simulate the complex wavefield that could result from the combination of stope and faulting sources. It was found that the source parameters calculated from synthetic seismograms generated by a shearing source below a stope were significantly influenced by the stope.

The seismic moment computed from the seismograms exceeded that of the moment of the fault by itself, indicating that the stope is contributing to the overall radiated wavefield.

Other effects were an increase in the PPVs in the vicinity of the stope; the introduction of longer wavelengths into the wavefield; an increase in the source radius and reduction of the stress drop; and an increase in the complexity of the waveforms.

This work provides a possible explanation of the second corner frequency often observed in the spectra of seismograms recorded in South Africa platinum mines. This has implications for the accurate determination of source parameters and the assessment of the intensity of shaking in stopes.

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Journal of the Southern African Institute of Mining and Metallurgy, vol. 115, no. 11. pp. 1121–1126. u The Journal of the Southern African Institute of Mining and Metallurgy

Development of a remote-controlled rockbolting system for narrow-seam hard-rock mines D.M. O’Connor1 and T. Sertic2 Affiliation: 1 Minova, South Africa.

2 Dok-ing Africa (Pty) Ltd, South Africa.

Correspondence to: D.M. O’Connor

Email:

donald.oconnor@minovaglobal. com

Dates:

Synopsis This development of a semi-automated, remotely-controlled rockbolting system for use in hard-rock mines with a mining height of between 0.9 m and 1.2 m is described. The rockbolting system required development of fully mechanized, remote-controlled rockbolting rig, novel rockbolts, and a pumpable, fast-acting resin grout to secure the bolts.

The rockbolter is one component of an equipment suite enabling full mechanization of rock-breaking by blasting, clearing of the broken rock and rock support. Development started in 2012 and the bolter has been operating on a platinum mine since 2017. Deployment of further equipment suites is planned for 2019.

Keywords rockbolting, mechanization, automation, narrow reef, pumpable resin.

Received: 29 Jul. 2019 Revised: 28 Nov. 2019 Accepted: 29 Nov. 2019 Published: January 2020

How to cite:

O’Connor, D.M. and Sertic, T. Development of a remotecontrolled rockbolting system for narrow-seam hard-rock mines.

The Southern African Insitute of Mining and Metallurgy DOI ID: http://dx.doi.org/10.17159/24119717/851/2020

This paper was first presented at the Deep Mining 2019 Conference, 24–25 June 2019 Misty Hills Conference Centre, Muldersdrift, Johannesburg, South Africa.

Introduction An estimated 90% of South Africa’s gold-bearing reefs are less than 1 m thick (Joughin, 1976). A large mineral resource therefore lies in seams that are becoming increasingly uneconomic to extract because of the grade dilution caused by raising the mining height to suit currently available mechanized mining equipment (Harper, 2008). A similar situation applies to platinum resources.

Historically in South Africa, mining of these low seams has been carried out by labour-intensive methods, with little equipment beyond hand-operated rock drills. However, the arduous and hazardous work environment is becoming increasingly unattractive to both the workforce and to mine operators.

Meanwhile, globally, major mining companies are striving to increase safety by removing people from the immediate vicinity of the operations, and to increase productivity by better integration of the phases of the regular mining cycle to reduce cycle times (Lynch and White, 2013). Attaining both objectives requires going beyond mechanization to high degrees of automation and/or remote control of equipment.

These factors present a challenge to South African mine operators and their equipment suppliers as mechanization of the low-seam, hard-rock mining environment has proven difficult and successes have been few (Pickering and Ebner, 2006; Harper, 2008).

This paper describes the development of a semi-automated, remotely-controlled rockbolting system for use in hard-rock mines with a mining height of between 0.9 m and 1.2 m. The rockbolting, system required development of fully mechanized, remote-controlled rockbolting rig, novel rockbolts and a pumpable, fast-acting resin grout to secure the bolts.

The introduction of systematic rockbolting has resulted in a decrease of rock-related accidents but the many manual operations required in drill-steel and bolt handling, in confined spaces and close proximity to high-powered equipment, has led to an increase in injuries (particularly hand injuries) to the rockbolting operators themselves (Makusha: 2015). The introduction of remote-controlled equipment has the potential to remedy this situation.

The rockbolter is one component of an equipment suite enabling full mechanization of rockbreaking by blasting, clearing of the broken rock, and rock support (the Ultra Low Profile Project – ULP). Development started in 2012 and the bolter has been operating on a platinum mine since 2017.

Deployment of further equipment suites is planned for 2019.

User requirement specifications for the equipment To achieve the production targets of the fully mechanized mining operation, the key performance parameters for the bolting operation were set as follows.

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Development of a remote-controlled rockbolting system for narrow-seam hard-rock mines ➤ Nominal bolting speed of 8 minutes between collaring one hole and collaring of the following hole ➤ Total cycle time to drill 30 bolts (1.6 m drilling) shall not exceed 2 hours ➤ Two 30 m panels should be drilled in one production shift.

In addition, there were comprehensive specifications regarding safety of operations and functionality in the envisaged low stope environment.

Performance and operational requirements for the rockbolts The standard support design for the stopes in the test area uses 1.6 m long deformed bar rockbolts, either 20 mm or 18 mm diameter. The bolts themselves have a nominal ultimate tensile strength (UTS) of 170–200 kN (RSC Ekusasa, 2007) and must achieve a pull-out load of at least 100 kN on 250 mm of resin bond in the short encapsulation pull test (SEPT) method generally used in the South African platinum mining industry.

Most rockbolts used in South Africa are made from steel with a UTS of 550–600 MPa, with steel up to 850 MPa for more advanced bolts.

Rockbolts to be used with the ULP project had to achieve at least the same performance as the conventional deformed bar rockbolts. Analysis of conventional rockbolting operations and rockbolts showed that the bolts were not well suited to the mechanized, automated bolting called for. ➤ The need to install 1.6 m long bolts in a mining height of <1.2 m indicated that the drill steels used would have to be coupled ➤ The rockbolts themselves would also have to be coupled or flexible (cable anchors) ➤ Extraction of the coupled drill steel segments after drilling each hole and storage so that they would be available for the next hole appeared to be a difficult process to mechanize and automate ➤ Installation of cable anchors is also a difficult process to fully mechanize and automate ➤ Handling of conventional resin or grout capsules is equally difficult to mechanize and automate because of their loss of rigidity. Although this is normally considered to be an ageing issue, O’Connor (2014) showed that resin capsules lose rigidity simply by being transported to a region of higher ambient pressure, such as in a deep mine ➤ The normal bolting cycle of drill – remove drill steels – insert grout capsules – insert bolt – spin bolt – tighten bolt is time-consuming and presents several situations (e.g. drill steels failing to extract), requiring human operator intervention.

An alternative that presented itself was ‘self-drilling rockbolts’ (SDRs, also known as SDAs – ‘self-drilling anchors’).

An SDR consists of a hollow steel rod (or coupled rods) with a sacrificial drill bit. The steel rod serves as the drill steel during the

drilling phase. It is left in the drill-hole and the central flushing hole is used to inject cement or resin grout to fill the external annulus between the steel rod and the rock, so fixing the rod into the hole. The rod then functions as a rockbolt. The use of SDRs is well established in the mining and construction industries (Minova-MAI, 2017) for rockbolting in very unstable ground, where the drill-holes close or collapse between withdrawal of the drill rod and insertion of a rockbolt.

SDRs are made of high-grade steel as they need to withstand the stresses of drilling and the design loads in their service as rockbolts. Typical steel specification for an SDR is a UTS of 600– 860 MPa (Minova-MAI, 2017). The external surface is deformed into a continuous coarse thread profile, for example a ‘rope’ thread with pitch of 12 mm and depth of 4.8 mm. The threaded profile increases axial shear resistance between the SDR and the surrounding grout, and is also used to attach accessories such as the sacrificial drill bit, centralizers, and couplings.

The use of commonly-available SDRs was considered but it became apparent that they were unsuitable. Conventional SDRs use external sleeve couplings to couple segments together. The sleeve couplings have a substantially larger diameter than the SDR itself, forcing the use of a larger bit and creating a large annulus around the bolt to be filled by the grout. Typical data for a 25 mm diameter SDR system is presented in Table I (MinovaMAI, 2017).

Sleeve couplings are also expensive. In the mainstream use of SDRs, the rods are typically 2–4 m long, so few couplings are needed to make up a drill string. However, in this project, the maximum length of the SDR segments was limited to 400 mm, so four couplings would be needed for a 1.6 m bolt.

An alternative type of SDR was sought. We found an unusual SDR rod manufactured in South Africa. Termed ‘NCA steel’ it is a 25 mm diameter drill steel with external rebar-type deformations. It had been manufactured on a small scale for specialized rehabilitation of concrete structures. The central flushing hole is only 11 mm in diameter, leaving a cross-sectional area of 396 mm2 – greater than that of the 20 mm conventional solid rockbolt. NCA steel had not been coupled previously. NCA steel has a minimum tensile strength of 940 MPa (ArcelorMittal, 2017). The high strength combined with the larger cross-sectional area allowed design of a taper-threaded internal coupling (see Figure 1). This avoided the hole diameter increase associated with conventional sleeve couplings.

Prototype NCA rods with the internal taper-threaded couplings were made up and tensile strength tests were conducted at a SANAS-accredited test facility. Minimum breaking load was 176 KN. This qualified the coupled NCA steel as being strong enough for the SDR component of the rockbolting system.

Drilling method – percussion or rotary?

Early in the project a decision had to be taken on the drilling method to use: percussion or rotary, as the choice was fundamental to the design and equipping of the rig as well as to configuration of the rockbolts/drill rods.

Table I

25 mm diameter grouted conventional SDR system Bolt dia. (mm)

Bolt UTS (kN)

Coupling dia. (mm)

Drill bit dia. (mm)

Grout volume for 1.6 m bolt (l)

150

33.7

42

1.43

25

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The Journal of the Southern African Institute of Mining and Metallurgy

Development of a remote-controlled rockbolting system for narrow-seam hard-rock mines

Figure 2—General view of drilling test rig

Figure 1—Cutaway view of taper-threaded internal coupling

Compressive strengths (UCS) of hangingwall rocks in South Africa’s gold and platinum mines are in the range of 120 MPa to over 250 MPa. Conventional production drilling in such rock is by percussion drills (Pickering and Ebner, 2001). There was limited data available on use of rotary drilling in hard rock. In 2003 one of the authors tested rotary drilling in the hangingwall at a platinum mine, using a hand-held bolter (‘Turbo-bolter’) and found that it was feasible.

Comparison of rotary and percussion drilling was carried out and the findings are summarized in Table II. The comparison showed that rotary drilling was conceptually more suited to integration into the compact, low-height bolter rig but actual drilling capability and equipment specification were still unknown.

To confirm the feasibility of routine rotary drilling in hard rock and to gather data for equipment design, an instrumented drilling test rig was built (Figure 2).

The test rig enabled drilling with controllable thrust and rotation speed, while measuring instantaneous penetration rate and torque. Norite (UCS 200 MPa) was used for the drilling trials.

NCA drill rods were made up with shaped heads, into which tungsten carbide (TC) inserts were brazed (Figure 3). The TC inserts were 30 mm wide, resulting in holes with a diameter 5 mm greater than the 25 mm diameter NCA drill rods, for flushing of the drill chippings. Water was used for flushing.

Using the test rig, 56 holes were drilled between February and April 2013. This demonstrated that:

Figure 3—Drill bit for rotary drilling

➤ Rotary drilling of norite with standard TC borers is feasible.

Penetration rates exceeding 1.5 m/min were consistently achieved and the TC inserts lasted the required 1.6 m of drilling depth (Figure 4) ➤ The NCA steel and the internal taper-threaded couplings were successful as drill rods.

Figure 4—Enlarged view of drill bit after drilling a 1.6 m hole

Extensive data on the penetration rate versus thrust and torque was obtained for design of the drilling head on the bolter and setting operating parameters. The best results were obtained

Table II

Comparison of rotary versus percussion drilling Issue

Technology risk Power source Vibration levels and impact on surrounding equipment Sound level Drifter length

Rotary

Drilling Technology

High – limited experience and data available in hard rock Electric: suitable for battery supply Low: low impact on surrounding equipment Lower – can be made < 85 dBa Can be made compact

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Percussion Low: well established technology in hard rock Hydraulic : large power pack required; Very high; surrounding equipment must be rugged High to very high (>95 dBa) Long – typically over 600 mm JANUARY 2020

93 ◀

Development of a remote-controlled rockbolting system for narrow-seam hard-rock mines at high rotation speed (700 r/min) and thrust in the range 40– 50 kN (O’Connor, 2013). A bilinear regression calculation on drill penetration rate versus rotation speed and thrust in the ranges tested gave:

Penetration rate (mm/min) = 1.29 r/min + 4.44 thrust (kN) In 2018 a further series of drilling tests was carried out using an actual bolter, drilling into a large block of norite set up in a surface workshop. The results are reported in Appendix I.

Grouting of the self-drilling rockbolts The user requirement of short hole-to-hole cycle time required a fast-setting grout. The bolter rig needed to move on to the following hole within seconds of completing an installation and the grout had to build strength rapidly to provide effective support. Speed had to be balanced with a working time long enough for the grout to be pumped through the bolt and fill the hole before setting.

Conventional bolted stope support uses resin capsules with a 30 second or 60 second setting time (Maepa and Zvarivadza, 2017) and these times formed the benchmark for performance of the pumped grout system. At the time of development (2012– 2104) there were no cement grouts available that met these requirements. A two-component resin grout had been developed (Richter, 2005) for use with SDRs and we decided to test this material. The resin grout is an organo-silicate system, supplied as two liquids with viscosities in the range 150–300 mPa.s (Minova Carbotech, 2017). When the two components are vigorously mixed, a two-stage chemical reaction takes place. In the first stage, the mixture thickens and becomes thixotropic, but is still pumpable. This prevents the material from flowing out of the hole and ensures that the annulus around the bolt is completely filled.

In the second stage, polymerization of the organic phase and precipitation of solid silica take place, resulting in a solid grout.

The resin grout first used had an initial hardening time (i.e. the time at which the bolter rig could release the bolt) of 60 seconds and reached full strength in 6 hours. Discussions with the manufacturer prompted further development and these times have now been shortened to 20 seconds and 60 minutes respectively. Variants of the same resin grout are being used in automated rockbolting developments elsewhere in the world (Bray and Johnsson, 2019).

The fast reaction time of the resin requires near in-situ mixing. For this, single-use static mixers are incorporated into the base of each bolt. Workshop trials carried out to determine the length of static mixer required to produce complete mixing showed that an ‘X’ type static mixer 100 mm in length was adequate (Figure 5).

JANUARY 2020

Testing and qualification Field short encapsulation pull testing Individual segments of the SDRs were installed in 250 mm deep holes drilled in the hangingwall at a platinum mine near Rustenburg in July 2015. The holes were 30 mm diameter and were pre-drilled using a conventional roofbolter. After installation, the bolts were pulled with a hand-operated hydraulic ram at different times after installation. The results are shown in Table IV. Salient outcomes from the tests were: ➤ There were many aspects of the installation and testing process that had to be learned and mastered before consistent results were achieved Table III

Resin grout properties (at 25°C) Property

Component A Component B Mixed grout

Major constituent Sodium Silicate Poly-isocyanate Viscosity (mPa-.s) 200–300 140–240 Relative density 1.43 1.16 Initial thickening (s) Initial set (s) Shore D hardness at 30 minutes

3–10 20–100 60

Table IV

Underground SEPT results

Hole no.

3 3B

Cure time (min)

Max. load (KN)

Notes

90

120

1

1440

150

2

4

1440

150

2

5

1440

125

2 2

6

1440

150

7

1440

150

2

7

1440

160

2

15

1440

150

2

16

1440

110

17

1440

100

18

1440

60

19

1440

75

1

20

1440

75

2

1A

56

40

2

2A

63

200

2

3A

76

170

2

4A

88

230

2

5A

92

160

2

6A

98

200

2

7A

102

200

2

8A

107

200

2

10A

51

110

3

11A

42

200

2

Notes: 1. Installation failure 2. Test stopped before failure 3. Rock failed

Figure 5—‘X’ type static mixer elements

▶ 94

The properties and performance of the resin grout used in 2018 are shown in Table III.

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Development of a remote-controlled rockbolting system for narrow-seam hard-rock mines ➤ Both the resin bond and the bolts themselves achieved the design loads.

Proof-of-concept trials, 2015 The complete rockbolting system was subjected to a proof-ofconcept (POC) trial at the same platinum mine. After the initial development of operating procedures, measurements were made of cycle times. A typical cycle is shown in Table V. The POC trial showed that the ULP bolting system was able to achieve close to the user specification of installing one SDR in 8 minutes or less.

Excluding downtime and externally-caused delays the drilling times achieved were between 06:49 and 07:22 minutes.

Current status The system has been in use at a platinum mine on the eastern limb of the Bushveld Complex since 2017. Since 2015 the bolter rig has undergone significant evolution on the basis of operational experience. Figure 6 and 7 show the 2014 and 2019 versions respectively – the later version is more compact, lighter, and provides easier access for maintenance. The two drilling booms each incorporate staking rods that stabilize the booms during drilling, ensuring consistent alignment between the SDR segments and between the base of the drill string and the drilling head/resin injection ports.

Table V

Drilling and grouting cycle times Start time

End time

Total time

Move machine

Activity

0

0

00:00:00

Line up booms

11:33:55

11:34:49

00:00:54

Load 1st SDR

11:35:22

11:35:52

00:00:30

Drill 1st SDR

11:35:52 11:37:00 00:01:08

SDR Rod Installation

Load 2nd SDR

11:37:00

Drill 2nd SDR

11:37:15 11:38:57 00:01:42

11:37:15 11:39:15

00:00:15

Load 3rd SDR

11:38:57

Drill 3rd SDR

11:39:15 11:41:31 00:02:16

Load 4th SDR

11:41:31

Drill 4th SDR

11:41:48 11:43:34 00:01:46

Resin injection

11:43:34

Total time to install SDR

11:41:48

00:00:18

11:43:44

00:00:17 00:00:10

00:08:22

Figure 6—2014 version of bolter rig The Journal of the Southern African Institute of Mining and Metallurgy

Figure 7—2019 version of the bolter rig

As mentioned previously, the rockbolter is part of an equipment suite. The stope layout was re-designed for compatibility with the strengths and limitations of the equipment suite. The bolter is steered manually (using radio remote control) when moving between stope panels. The layout avoids the need to cross obstructions such as gullies.

Conclusions A multi-year development programme involving users and equipment suppliers has resulted in a working system for automated and remote-controlled rockbolting in hard-rock mines with tabular stopes less than 1 m high. The cycle times already achieved and the removal of the operators from the immediate vicinity of the operation have the potential to bring about improvements in the productivity and safety of existing operations, mainly located in South Africa. In the longer term, the suite of ultra-low-profile mining equipment may allow some narrow orebodies, currently considered as uneconomic to mine, to be re-classified as mineral reserves.

References

Areclor-mittal. 2017. Material test certificates. Vereeniging, South Africa.

Bray, P. and Johnsson, A. 2019. Case study: LKAB Malmberget, self-drilling anchors and pumpable resin. Proceedings of DeepMine 2019. Southern African Institute of Mining and Metallurgy, Johannesburg Harper, G.S. 2008. Nederburg miner. Proceedings of the Narrow Vein and Reef Symposium. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 2–4.

Joughin, N.C. 1976. Potential for the mechanization of stoping in gold mines. Journal of the South African Institute of Mining and Metallurgy, vol. 76, no. 1. https:// www.saimm.co.za/journal/v076n06p285.pdf Lynch, M. and White, B. 2013. Brave new world of autonomous mining systems.

Proceedings of the World Gold Conference, Brisbane. Australasian Institute of Mining and Metallurgy, Melbourne. pp. 157–166.

Maepa, T. and Zvarivadza, T. 2017. Installation of resin-grouted rockbolts in hardrock mining: Challenges and solutions for improved safety. Journal of the Southern African Institute of Mining and Metallurgy, vol. 117, no. 4. pp. 329–336. https://www.saimm.co.za/journal/v117n04p329.pdf Makusha, G. 2015. Personal communication.

Minova-Mai. 2017. Self-drilling systems product catalogue. Feistritz/Drau, Austria. pp. 3–10, 16.

Minova Carbotech. 2017. Carbothix 150709 technical data sheet. Essen Germany.

O’connor, D. 2014. Effect of atmospheric pressure on resin capsule rigidity.

Proceedings of the SANIRE Coalfields Branch Symposium, November 2014.

South African National Institute of Rock Engineering O’connor, D. 2013. Interim report on rotary drilling trials in norite. Minova Africa (Pty) Ltd, Johannesburg.

Pickering, R. and Ebner, B. 2001. Hard rock cutting and development of a continuous mining machine for narrow platinum reefs. Proceedings of the 6th International Symposium on Mine Mechanisation and Automation. Southern african institute of mining and metallurgy, Johannesburg. https://www.saimm.co.za/journal/ v102n01p019.pdf .

Richter, A. 2005. A method for embedding rock anchors. Australian patent application 2005297473 b2, 2005.

Rsc ekusasa. 2007. Product datasheet. Rsc Ekusasa Mining, Johannesburg.

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Development of a remote-controlled rockbolting system for narrow-seam hard-rock mines Appendix I Second series of rotary drilling trials In December 2018 a second programme of rotary drilling trials was carried out as part of ongoing system improvement. This programme used an actual bolter to carry out the drilling; the results could therefore be applied directly to optimize drilling parameters and practice.

The holes were drilled into a large block of quarried norite, supported on a frame to simulate the hangingwall of a stope (Figure I-1).

Two series of tests were carried out, one with ’soft collaring’ of the bit and one without. Soft collaring used a slow approach of the bit to the rock face and a gradual ramp-up of thrust and rotation speed. It was intended to reduce the likelihood of the bit shattering on contact with the rock.

The tests measured drilling time against rotation speed at different thrust settings. Drill bit condition was recorded after each hole.

The results are summarized in Figures I-2 and I-3.

Findings

➤ Increasing thrust force results in a reduction in drilling time, but when thrust exceeded 3 t (30 kN) there was a heightened risk of premature bit failure. ➤ For each thrust setting, there was an indication of an optimal rotation speed. When the rotation speed exceeded 600 r/min there was heightened risk of premature bit failure. ➤ There was no significant difference in time when drilling with soft collaring and not using soft collaring. ➤ The drill bits were capable of drilling at least 1.6 m in the hard norite, provided that the thrust was limited to 3 t (30 kN) and the rotation speed less than 600 r/min. u

Figure I-1—ULPSR 002 in drilling position

Figure I-2 –Average times with collaring

Figure I-3 –Average times without collaring

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REVITALISING EXPLORATION ACTIVITY IN SOUTHERN AFRICA POTENTIAL FOR EXPLORATION

Date: 27-28 May 2020 Venue: Glenhove Events Hub Melrose Estate, Johannesburg

Exploration is a critical component of the development and sustainability of the minerals industry, in any resource endowed country.

Southern Africa still has vast untapped mineral resources, yet the level of exploration is at an all-time low.

• • • • • • •

Policies and Legal Issues Brownfields/Greenfields Exploration What are the potential successes Strategic/Local Success Technology Data Modelling Financing and Funding

SPONSORSHIP Sponsorship opportunities are available.

Companies wishing to sponsor or exhibit should contact:

Conference Coordinator: Camielah Jardine, Head of Conferencing

E-mail: camielah@saimm.co.za Tel: +27 11 834-1273/7

NATIONAL & INTERNATIONAL ACTIVITIES 2020 23–26 February — 2020 SME Annual Conference & Expo Phoenix, Arizona, USA Website: http://www.smeannualconference.com 1–4 March 2020 — PDAC 2020 Convention Metro Toronto Convention Centre, Toronto, Canada Contact: info@pdac.ca / convention@pdac.ca Website: https://www.pdac.ca/convention 8 March 2020 — International Womens Day Luncheon Series 2020 Sydney Australia Website: https://www.internationalwomensday.com 10–14 March 2020 — CONEXPO 2020 Las Vegas, NV Website: https://www.conexpoconagg.com 17–18 March 2020 — 5th Young Professionals Conference 2020 The Canvas, Riversands, Fourways Contact: Camielah Jardine Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za 25–26 March 2020 — GMG Forum Glenhove Events Hub, Melrose Estate Contact: Gugu Charlie Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: gugu@saimm.co.za Website: http://www.saimm.co.za 25–27 March 2020 — Underground Operations 2020 Perth Convention and Exhibition Centre, Perth Western Australia Website: https://undergroundoperators.ausimm.com 1–2 April 2020 — Mines and Money Asia 2020 Hong Kong Convention & Exhibition Centre, Hong Kong E-mail: asia@minesandmoney.com Website: https://asia.minesandmoney.com 22 April 2020 — Reconnecting with the Future Workshop The Equinox Leadership and Innovation Centre, Sandhurst Contact: Camielah Jardine Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za

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21–23 April 2020 — MiningWorld Russia 24th International exhibition of machines and equipment for mining, processing and transportation of minerals Crocus Expo, Moscow, Russia Website: https://www.miningworld.ru/en-GB/ 3–6 May 2020 — CIM Convention and Expo Vancouver, Canada Website: https://convention.cim.org 10–13 May 2020 — Uranium 2020 Saskatoon, Canada Website: https://u2020.metsoc.org 10–13 May 2020 — 8th Annual Current Trends in Mining Finance (CTMF) Conference 2020 New York Website: https://community.smenet.org 12–13 May 2020 — Digitalization in Mining Conference 2020 The Canvas, Riversands, FourwaysContact: Gugu Charlie Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: gugu@saimm.co.za Website: http://www.saimm.co.za 23–30 May 2020 — ALTA 2020 Nickel-Cobalt-Copper, Uranium-REE, Gold-PM, In Situ Recovery, Lithium & Battery Technology Conference & Exhibition Perth, Australia Contact: Allison Taylor Tel: +61 411 692 442 E-mail: allisontaylor@altamet.com.au Website: https://www.altamet.com.au/conferences/alta2020/ 25–29 May 2020 — The 11th International Conference on Molten Slags, Fluxes and Salts 2020 The Westin Chosun Seoul Hotel, Seoul, Korea Website: http://www.molten2020.org 27–28 May 2020 — Revitalising Exploration Activity in Southern Africa 2020 Glenhove Events Hub, Melrose Estate Contact: Camielah Jardine Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za 3–5 June 2020 — ROLLS6 2020 London, UK, Website: http://www.rolls6.org/home 7–11 June 2020 — NAT2020 North American Tunneling Conference Nashville, Tennessee Website: http://www.natconference.com

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NATIONAL & INTERNATIONAL ACTIVITIES 9–11 June 2020 — Diamonds – Source to Use — 2020 Conference The Birchwood Hotel & OR Tambo Conference Centre Contact: Camielah Jardine Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za 14–17 June 2020 — 2nd European Mineral Processing and Recycling Congress – EMPRC 2020 Aachen, Germany Contact: Jürgen Zuchowski, Email: gdmb@gdmb.de Website: https://emprc.gdmb.de 22–23 June 2020 — Renewable Solutions for an Energy Intensive Industry Colloquium Kathu, Northern Cape Contact: Camielah Jardine Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za 24–25 June 2020 — 3rd School on Manganese Ferroalloy Production 2020 Kathu, Northern Cape Contact: Gugu Charlie Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: gugu@saimm.co.za Website: http://www.saimm.co.za

Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za 4–7 October 2020 — Massmin 2020 Eight International Conference on Mass Mining Santiago, Chile, Contact: J.O. Gutiérrez Tel: (56-2) 2978 4476 Website: www.minas.uchile.cl 18–22 October 2020 — IMPC XXX International Mineral Processing Congress 2020 Cape Town International Convention Centre, Cape Town Contact: Camielah Jardine Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za 18–22 October 2020 — IMPC Congress Cape Town International Convention Centre, Cape Town Contact: Camielah Jardine Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za

22–23 July 2020 — 5th Mineral Project Valuation Colloquium Glenhove Events Hub, Melrose Estate Contact: Gugu Charlie Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: gugu@saimm.co.za Website: http://www.saimm.co.za 18–19 August 2020 — SAMCODES Conference The Birchwood Hotel & OR Tambo Conference Centre Contact: Camielah Jardine Tel: +27 11 834-1273/7 Fax: +27 11 838-5923/833-8156 E-mail: camielah@saimm.co.za Website: http://www.saimm.co.za 20–21 August 2020 — Mine Health and Safery Conference Misty Hills Country Hotel & Conference Centre Contact: Camielah Jardine Tel: +27 11 834-1273/7

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Company affiliates The following organizations have been admitted to the Institute as Company Affiliates 3M South Africa (Pty) Limited

Expectra 2004 (Pty) Ltd

MSA Group (Pty) Ltd

AECOM SA (Pty) Ltd

Exxaro Coal (Pty) Ltd

Multotec (Pty) Ltd

AEL Mining Services Limited

Exxaro Resources Limited

Murray and Roberts Cementation

Air Liquide (PTY) Ltd

Filtaquip (Pty) Ltd

Nalco Africa (Pty) Ltd

Alexander Proudfoot Africa (Pty) Ltd

FLSmidth Minerals (Pty) Ltd

Namakwa Sands(Pty) Ltd

AMEC Foster Wheeler

Fluor Daniel SA ( Pty) Ltd

Ncamiso Trading (Pty) Ltd

AMIRA International Africa (Pty) Ltd

Franki Africa (Pty) Ltd-JHB

New Concept Mining (Pty) Limited

ANDRITZ Delkor (Pty) Ltd

Fraser Alexander (Pty) Ltd

Northam Platinum Ltd - Zondereinde

Anglo Operations Proprietary Limited

G H H Mining Machines (Pty) Ltd

Opermin Operational Excellence

Anglogold Ashanti Ltd

Geobrugg Southern Africa (Pty) Ltd

Optron (Pty) Ltd

Arcus Gibb (Pty) Ltd

Glencore

ASPASA

Hall Core Drilling (Pty) Ltd

Paterson & Cooke Consulting Engineers (Pty) Ltd

Atlas Copco Holdings South Africa (Pty) Limited

Hatch (Pty) Ltd Herrenknecht AG

Aurecon South Africa (Pty) Ltd

HPE Hydro Power Equipment (Pty) Ltd

Aveng Engineering Aveng Mining Shafts and Underground Axiom Chemlab Supplies (Pty) Ltd Axis House Pty Ltd Bafokeng Rasimone Platinum Mine Barloworld Equipment -Mining BASF Holdings SA (Pty) Ltd BCL Limited Becker Mining (Pty) Ltd BedRock Mining Support Pty Ltd BHP Billiton Energy Coal SA Ltd Blue Cube Systems (Pty) Ltd Bluhm Burton Engineering Pty Ltd Bond Equipment (Pty) Ltd

Immersive Technologies IMS Engineering (Pty) Ltd Ingwenya Mineral Processing (Pty) Ltd

Coalmin Process Technologies CC Concor Opencast Mining

Royal Bafokeng Platinum

Leco Africa (Pty) Limited

Roytec Global (Pty) Ltd

Leica Geosystems (Pty) Ltd

RungePincockMinarco Limited

Longyear South Africa (Pty) Ltd

Rustenburg Platinum Mines Limited

Lull Storm Trading (Pty) Ltd

Salene Mining (Pty) Ltd

Maccaferri SA (Pty) Ltd

Sandvik Mining and Construction Delmas (Pty) Ltd

Magnetech (Pty) Ltd Magotteaux (Pty) Ltd

MBE Minerals SA Pty Ltd MCC Contracts (Pty) Ltd

Metalock Engineering RSA (Pty)Ltd

CRONIMET Mining Processing SA Pty Ltd

Redpath Mining (South Africa) (Pty) Ltd Rosond (Pty) Ltd

MDM Technical Africa (Pty) Ltd

Council for Geoscience Library

Rand Refinery Limited

Kudumane Manganese Resources

MD Mineral Technologies SA (Pty) Ltd

Concor Technicrete

Ramika Projects (Pty) Ltd

Rocbolt Technologies

Maxam Dantex (Pty) Ltd

CGG Services SA

Precious Metals Refiners

Joy Global Inc. (Africa)

Maptek (Pty) Ltd

CDM Group

Polysius a Division of Thyssenkrupp Industrial Sol

Ivanhoe Mines SA

Malvern Panalytical (Pty) Ltd

Bouygues Travaux Publics

Perkinelmer

Metorex Limited

Sandvik Mining and Construction RSA (Pty) Ltd SANIRE Schauenburg (Pty) Ltd Sebilo Resources (Pty) Ltd SENET (Pty) Ltd Senmin International (Pty) Ltd Smec South Africa Sound Mining Solution (Pty) Ltd SRK Consulting SA (Pty) Ltd Time Mining and Processing (Pty) Ltd

CSIR Natural Resources and the Environment (NRE)

Metso Minerals (South Africa) Pty Ltd

Data Mine SA

MineARC South Africa (Pty) Ltd

Digby Wells and Associates

Minerals Council of South Africa

DRA Mineral Projects (Pty) Ltd

Minerals Operations Executive (Pty) Ltd

DTP Mining - Bouygues Construction

MineRP Holding (Pty) Ltd

Verni Speciality Construction Products (Pty) Ltd

Duraset

Mintek

Webber Wentzel

Elbroc Mining Products (Pty) Ltd

MIP Process Technologies (Pty) Limited

Weir Minerals Africa

eThekwini Municipality

MLB Investment CC

Welding Alloys South Africa

Ex Mente Technologies (Pty) Ltd

Modular Mining Systems Africa (Pty)Ltd

Worley

▶ xii

JANUARY 2020

Micromine Africa (Pty) Ltd

VOLUME 120

Timrite Pty Ltd Tomra (Pty) Ltd Ukwazi Mining Solutions (Pty) Ltd Umgeni Water

The Journal of the Southern African Institute of Mining and Metallurgy

5TH

YOUNG PROFESSIONALS

CONFERENCE

A SHOWCASE OF EMERGING RESEARCH AND INNOVATION IN THE MINERALS INDUSTRY

17-18 MARCH 2020

THE CANVAS, RIVERSANDS, FOURWAYS 2 CPD POINTS Innovation and research into mining technology is necessary to position Africa as a world leader in minerals production and beneficiation. The Young Professionals Council is pleased to host a unique, two-day conference that will showcase a broad range of emerging research and innovation from young professionals in the metals and minerals industry. Presentations will focus on new technology, tools and techniques relevant to exploiting Africa’s mineral resources safely, competitively and sustainably.

This conference should be of value to all professionals across the entire minerals industry value chain, including:

OBJECTIVES •

WHO SHOULD ATTEND

a broad range of topics covering the entire mining value-chain will give a quick sense of developments in the field of mining and metallurgy

•

a large body of research at Masters, PhD and Post-doctoral level will give insights into emerging themes and advances in the minerals and metals knowledge-areas

•

a focus on innovative practices, technological applications and case-studies from mining operations and research institutions will give the practicing professional an opportunity to learn about new tools and techniques relevant to their work

•

a gathering of diverse professionals within the metals and minerals community will give delegates an opportunity to obtain exposure, build reputations, further their careers and network with peers and leaders in the African Minerals Industry

• • • • • • • • • •

All metallurgical fields Exploration Geology Geotechnical engineering Leadership/management/government/ community Mining Occupational Hygiene and SHE practitioners ICT experts Mechanical, electrical/electronic engineers Mineralogy

EXHIBITION/SPONSORSHIP Sponsorship opportunities are available.

Companies wishing to sponsor or exhibit should contact the Conference Co-ordinator.

FOR FURTHER INFORMATION, CONTACT: Camielah Jardine, Head of Conferencing

E-mail: camielah@saimm.co.za Tel: +27 11 834-1273/7 Web: www.saimm.co.za

MINING STUDIES

SMME

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