Improving construction site productivity using the Internet Of Things by Riham Zawil

IMPROVING CONSTRUCTION SITE PRODUCTIVITY USING THE INTERNET OF THINGS A thesis submitted to the University of Brighton in partial fulfilment of the requirement for the degree of MSc Project Management for Construction

SEPTEMBER 2019

JAD ZAWIL London, United Kingdom

Acknowledgements First and foremost, praises and thanks to God, the Almighty, for His showers of blessings throughout my research work to complete the research successfully. I want to express my deep and sincere gratitude to all the lectures of the MSc project management for construction course of the School of Environment and Technology of the University of Brighton, especially the course leader, Dr Kasim Gidado for his guidance, care and motivation to do this research. I owe my indebtedness to my supervisor, Dr Hannah Wood, for her dedication, knowledge and guidance, towards the completion of this dissertation. I am extremely grateful to my parents for their love, prayers, caring and sacrifices for educating and preparing me for my future. Also, I express my thanks to my sisters, brother, for their support.

Declaration I declare that the research contained in this thesis, unless otherwise formally indicated within the text, is the original work for the author. The thesis has not been previously submitted to this or any university for a degree and does not incorporate any material already submitted for a degree.

Signed: Date: 28/08/2019 Words count: 15,000 words

Abstract The focus of this research is on the Internet of Things (IoT) and Artificial Intelligence (AI) and how they can be utilised on construction sites to improve productivity in the United Kingdom. The productivity growth in the UK construction industry has barely improved in the last decade, which makes this study research essential to find new technological techniques that can be adopted to improve the productivity on construction sites. This research contains a critical review of literature designed, evaluating the challenges facing productivity, how the innovation of Internet of Things and Artificial Intelligence is transforming the construction industry and improving productivity, the barrier of the application of IoT and AI in the construction industry and how they IoT and AI can be combined to unlock the productivity growth. The research approach to this study include personal interviews with project managers to capture their view about the challenges facing productivity and their view about using the IoT in the construction industry to improve productivity. The results acknowledge that the challenges that are facing the productivity in the UK construction industry are focused on three major factors, 1) Human factors, 2) economic factors and 3) Cultural factors. The research proves that using the Internet of Things in the construction industry can improve productivity growth by saving time, money, increasing safety and delivering good quality but there is a high barrier to the application of IoT and AI in the construction industry. This barrier is the resistance of people. Keywords: Construction site productivity, Internet of Things, Artificial Intelligence, Productivity barriers.

Table of Contents Acknowledgments ................................................................................................................................. 2 Declaration............................................................................................................................................. 3 Abstract ................................................................................................................................................. 4 Table of Contents .................................................................................................................................. 6 Table of Charts...................................................................................................................................... 9 1

Chapter one: Introduction ......................................................................................................... 10

1.1

Background of the study......................................................................................................... 10

1.2

Research problem ................................................................................................................... 11

1.3

Reason for the research .......................................................................................................... 12

Research Aim and Objectives ............................................................................................................ 13 1.4

Outline methodology ............................................................................................................... 13

1.5

Scope of the study.................................................................................................................... 15

1.6

Outline structure ..................................................................................................................... 16

Chapter two: Literature Review ................................................................................................ 17

2.1

Introduction ............................................................................................................................. 17

2.2

What is productivity? ............................................................................................................. 17

2.3

How is productivity measured in the construction industry? ............................................. 17

2.4

Factors that affect productivity on construction sites: ........................................................ 19

2.5

What are the challenges that are facing productivity in the UK construction industry?. 20 2.5.1

Human factors: An ageing workforce, poor image of construction and lack of diversity. 21

2.5.2

Economic factor: Brexit ................................................................................................ 22

2.5.3

Cultural factors: inefficiencies due to the prevailing â&#x20AC;&#x153;vicious productivity cycle.â&#x20AC;? ..... 24

2.6

What is the Internet of things? .............................................................................................. 25

2.7

How did the IoT start?............................................................................................................ 25

2.8

What are the benefits of connecting devices with each other without involving humans? 26

2.9

How big is the IoT? ................................................................................................................. 27

2.10

What is IoT for Construction?............................................................................................... 28

2.11

What is artificial intelligence (AI) and how it is related to the IoT? .................................. 28

2.12

How can IoT and AI unlock the productivity on the construction site? ............................ 29 2.12.1

Using Machine control and autonomous construction equipment ................................ 30

2.12.2

Using Site monitoring sensors ...................................................................................... 31

2.12.3

Using Smart personal protective equipment (PPE) and wearable technology on-site .. 32

2.12.4

Using Building Information Modelling (BIM) ............................................................. 36

2.12.5

Using Intelligent prefab ................................................................................................ 37 5

2.12.6

Using Augmentation Reality for Better Visualization .................................................. 38

2.12.7

Using concrete sensors .................................................................................................. 39

Using Robots to boost productivity .............................................................................................. 39 2.12.8

Artificial Intelligence Will Make Jobsites More Productive ........................................ 40

Artificial Intelligence Will Address Labour Shortages ................................................................. 40 2.12.9

Artificial Intelligence can manage a whole project....................................................... 41

2.13

Side effects of the Internet of Things and the possible risk ................................................. 41

2.14

How IoT security and connectivity challenges can be mitigated? ...................................... 42

2.15

Challenges facing the implementation of IoT and AI in the construction industry .......... 42

2.16

The use of the Internet of things in other Industries ........................................................... 43

2.17

Conclusion ............................................................................................................................... 44

Chapter three: Research methodology, Data collection and Data analysis ........................... 45

3.1

Introduction ............................................................................................................................. 45

3.2

Research philosophy ............................................................................................................... 45

3.3

Research strategy .................................................................................................................... 48

3.4

Data Collection ........................................................................................................................ 49

3.5

Interviews................................................................................................................................. 50

3.6

Data analysis ............................................................................................................................ 51

3.7

Ethical considerations ............................................................................................................. 51

3.8

Summary.................................................................................................................................. 52

Chapter four: Interview finding and Analysis ......................................................................... 53

4.1

Introduction ............................................................................................................................. 53

4.2

Interview Findings .................................................................................................................. 53

4.3

Interview results and Analysis ............................................................................................... 54

Chapter five: Discussion ............................................................................................................. 59

5.1

Introduction ............................................................................................................................. 59

5.2

Discussion and review of key findings ................................................................................... 59

Chapter six: Conclusions and Recommendations .................................................................... 64

6.1

Scope of chapter ...................................................................................................................... 64

6.2

Research objectives: summary of findings and conclusions ............................................... 64

6.3

Overall conclusion ................................................................................................................... 69

6.4

Recommendations ................................................................................................................... 70

6.5

Limitation of the Research ..................................................................................................... 71

References .................................................................................................................................... 72

Appendices ................................................................................................................................... 79

8.1

Appendix 1: Ethics Checklist ................................................................................................. 79

8.2

Appendix 2: Interview Form .................................................................................................. 81 6

8.3

Appendix 2: table of interview ............................................................................................... 82

8.4

Appendix 4: Risk assessment Form ....................................................................................... 86

8.5

Appendix 5: Participant information sheet ......................................................................... 87

Table of Charts Chart 1 productivity growth- output per worker ............................................................................ 12 Chart 2 Comparing productivity growth between construction and the whole economy ........... 20 Chart 3: Most trending modules for students in the 2016-2017 academic year ........................... 22 Chart 4: Percentage of those working in the industry by age ......................................................... 23 Chart 5: Reasons people are not embracing IoT ............................................................................. 43

List of Acronyms

Artificial Intelligence

Augmentation reality

BIM

Building Information Modelling

CIOB

Chartered institute of Buildings

European union

ERP

Enterprise Resource Planning

ESRC

Economic and Social Research Council

GDP

Gross Domestic Product

IoT

Internet of Things

ONS

Office for national statistic

Project Manager

PPE

Personal Protective Equipment

VPN

Virtual private network

Virtual Reality

1 CHAPTER ONE: INTRODUCTION 1.1 BACKGROUND OF THE STUDY Many terms are used to describe productivity in the construction industry, such as unit-person hours rate, performance factors and production rate. In economic terms, as explained in the Office for National Statistics (ONS) Productivity Handbook, productivity is the rate of output per unit of input. Meaning that by creating more output, in term of quality and quantity for a giving input, will result in a higher living standard. (Brian Green, CIOB, 2016). Therefore, in order to increase productivity, we can achieve one of the followings: 1.

Reduce input for the same output,

Increase output for the same giving input,

A bit of both.

In construction, the output is expressed in volume, length and weight; while the input resources are usually in cost of labour or man-hours (Intergraph, 2012); In other words, construction productivity is the total value of construction (GDP) divided by the total number of hours (Hrs) worked, which means the duration of time the workers took to perform their tasks (script&go, 2018) . However, labours in construction sites have a significant effect on the productivity on site. Choosing the right labour to do a specific task at a given time and place, ensure the efficiency of the labour, which increases productivity. Furthermore, Mckinsey & company (2017) indicate that the construction sector has much to do in order to increase productivity growth. This includes the reshape of regulation, infusion of digital technology, and the use of advanced automation on site. Advancement of technology, with the use of the Internet of Things (IoT) and Artificial Intelligence (AI), enables the application in a variety of ways in numerous sectors of the 10

economy and helps to improve the productivity in these sectors. The construction industry may be lag behind in adopting these technologies mainly on the operations conducted on project sites.

1.2 RESEARCH PROBLEM Over the years, the UK construction industry has faced many challenges to improve its productivity by increasing value and saving time. Delivering a project on budget and on time is a challenge that has always plagued the construction industry (Ryan Tute, 2018). Most of the projects run over time, over budget, and the industry is slower than all others at adapting to changes, which costs companies billions. According to McKinsey, Large projects are taking up to 20% longer than scheduled to finish and are up to 80% over budget. A script & Go (2018) research indicated that the UK construction productivity has dropped by 20% since the last global financial crisis and that this is due to a combination of economic, cultural and human factors. Furthermore, a report from Mckinsey Global Institute (2017) found that the UK construction productivity has grown only 1% a year for the past two decades, compared with a 2.8% growth for the total world economy and 3.6% growth in manufacturing, which shows that the construction industry is stuck in a time warp.

180 160

140 120 100 80 60 40 20

0 1997 whole economy

2000

2007 construction

2010 manufacturing

2017 services

2019 production

Chart 1 productivity growth- output per worker (1997=100) (source=ONS)

The above chart from the office for national statistics (ONS) proves that construction productivity has barely improved comparing to other sectors, and it is affecting the growth of the whole economy's productivity. Furthermore, construction productivity decreased by 1.3% in Quarter 2 (Apr to June) 2019 largely reversing the increase of 1.4% in Quarter 1 (Jan to Mar) 2019. (ONS, 2019) Nevertheless, GDP from construction in the UK is ÂŁ28,324 million in the second quarter of 2019 comparing to the GPD from manufacturing, which is ÂŁ45,071 million. (trading economics, 2019)

1.3 REASON FOR THE RESEARCH As the UK construction industry suffers from a real productivity problem, especially on-site, this research seeks to identify this problem and provide adequate information and solutions to project managers and construction companies. Thus, this information can be used to improve 12

productivity on construction sites and prevent incidents from happening, mainly by using advanced technology. Furthermore, this research provides information on how IoT and AI can help deliver the project on time and budget while keeping excellent quality.

RESEARCH AIM AND OBJECTIVES This research aims to make recommendations on how the Internet of Things and Artificial Intelligence can be utilised on construction sites to improve productivity. In order to achieve this aim, five key objectives are addressed: 1. To Investigate the challenges that are facing productivity in the UK construction industry; 2. To Identify how the application of IoT and AI can unlock productivity growth and improve the management on construction sites; 3. To Classify the sophistication, use, advantages, and disadvantages of IoT and AI in the construction industry; 4. To Identify the barriers for the application of IoT and AI in the construction industry; 5. To make recommendations for how we can combine IoT with AI to achieve productivity growth

1.4 OUTLINE METHODOLOGY In this thesis, a construction engineering-based approach and primary and secondary data collection will be used to develop the research. The primary data will be collected using multiple personal interviews. While the secondary data will be collected using journals, articles, books, government publications, and websites.

In order to identify the challenges that are facing productivity on the construction sites in the UK, multiple personal interviews with construction and project managers in different construction and contractors company should be made. These interviews will ask about the problems they are facing on the sites and what procedures they are trying to use to increase productivity. In order to identify how the IoT and AI will affect the building environment, and to consider the side effects and sophistication, data collected from past case studies will be used to formulate part of the interviews. On the other hand, in order to determine how the IoT and AI can improve the management in construction sites; interviews will be conducted. These will comprise of a range of questions addressed and be posed to Project managers from different companies. By using interviews, data will be collected to identify what the problems that management in construction sites are facing. They will also enable understanding as to how technology can reduce those problems and improve the way management works. There will be barriers to the application of IoT and AI in the construction industries. In order to identify these barriers, interviews will be conducted to understand the reasons why project managers are not using new technology to improve productivity. Secondary data collection will be used to identify how the IoT and AI can unlock productivity growth and what are the side effect of using it. Artificial intelligence and the Internet of Things are two different things, IoT is about physical devices that are connected to the internet, and that can communicate among themselves using sensors or with the external environment using various devices that are capable of using the network, like vehicle to human. While Artificial Intelligence is a field in computer science in which the machines have the ability to learn things and make decisions 14

based on past experience and have the ability to mimic cognitive functions of a human. A recommendation will be given using research and case studies to see how we can combine IoT and AI to achieve productivity growth in the construction sites. Qualitative data collection will be used in this research and content analysis technique will be used to help identify the most important area to focus on for improvement after surveys and interviews. The primary resource required for this research is people, and construction companies since most of the data will be collected during interviews with people that work in the construction industry.

1.5 SCOPE OF THE STUDY This research aims to analyse productivity growth and the challenges facing the UK construction industry. Even though the construction industry has a range of ways in which it can improve, and there are different ways to improve productivity, the scope of this study is limited to the use of Internet of Things and Artificial Intelligence to improve productivity on the construction sites. This research will provide useful information for the construction companies and project managers on how the Internet of Things and Artificial intelligence can be combined with the construction industry and the benefits it can bring to improve the productivity on the construction sites.

1.6 OUTLINE STRUCTURE Chapter one (Introduction) discusses the background and the problems in the construction industry, indicating the necessity of this research. It also highlights the aim and objectives of the study; the need to identify new strategies to improve productivity in the construction sites and the methods used to develop the research. Chapter two (Literature review) focuses on the literature review, which is one of the key aspects of the study. It includes the definition and clear understanding of key operative words (Productivity, Internet of Things, Artificial Intelligence). The chapter will also identify how productivity is measured and bring to light the challenges facing it in the UK construction industry. It will also discuss how using IoT and AI devices can decrease these challenges and increase productivity. Chapter three (Research methodology, data collection and analysis methods) provides a detailed overview of the research methodology and the methods used for data collection and analysis. It also explains the rationale behind interviews as a primary data source rather than a questionnaire, followed by ethical considerations and data collection limitations. Chapter four (interview results) focuses on displaying the results of the interview surveys using qualitative analysis of interview data. Chapter five (discussion) contains the main section of the research that will discuss the interview results with the literature review Chapter six (conclusion and recommendation) captures the essence of the study and contains the conclusion, as well as the recommendations of the research. This chapter will clarify the suitability of the research and authenticate its results and outcomes of the research.

2 CHAPTER TWO: LITERATURE REVIEW 2.1 INTRODUCTION This chapter sets the benchmark for the research study on the topic: â&#x20AC;&#x153;How we can we improve productivity on UK construction sites using the internet of things and artificial intelligence?â&#x20AC;? The chapter starts by extracting information from existing literature to establish a clear definition of Productivity, the Internet of Things and Artificial Intelligence. This chapter delivers the new innovation in the construction industry using the IoT and AI and how it can unlock productivity growth on the construction sites, what are the barriers and challenges affecting the implementation of this technology in the construction industry and what are the disadvantages of using it.

2.2 WHAT IS PRODUCTIVITY? The simple explanation of productivity in a construction site is that the work is completed within the time, cost and quality framework. In other words, productivity is the rate of output per unit of input and measuring it can be hard and expensive in the construction industry.

2.3 HOW IS PRODUCTIVITY MEASURED IN THE CONSTRUCTION INDUSTRY? Measuring productivity growth in construction has been a classic challenge, mainly because reliable output deflators are scarce. (ASCE, 2016) Despite these challenges, it is essential to measure productivity in order to identify which areas need to be improved. Productivity is a measure of output/input or efficiency, rather than a measure of effectiveness, value or quality, and in that sense, we risk focusing on the wrong issue. (Don Ward, chief executive of construction excellence, brebuzz.net, 2017)

Nevertheless, there is no single simple measure; instead, there are a whole array of ways to measure productivity within the economy. The default measure tends to be labour productivity. Productivity can be measured as output per hour worked, or per job or worker, (value/person-hours). Construction productivity can be measured at macro and micro scale, the former at asset value (output achieved) for the sector and the latter as how much to a specific project asset. At macro scale productivity is typically ÂŁ generated per person-hour, and at micro-scale, it could be ÂŁ to construct versus m2 covered for a building. The micro-scale units will depend on the type of project, building or infrastructure, and are a measure of how much it cost to build, rather than the value created. (Simon Cross, brebuzz.net, 2017)

For example: precision plastic makes 5,000 products each week. Total weekly labour hours are 1,250. đ?&#x2018;&#x2122;đ?&#x2018;&#x17D;đ?&#x2018;?đ?&#x2018;&#x153;đ?&#x2018;˘đ?&#x2018;&#x; â&#x201E;&#x17D;đ?&#x2018;&#x153;đ?&#x2018;˘đ?&#x2018;&#x;đ?&#x2018; đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x; đ?&#x2018;¤đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2DC;

1250

Labour productivity = đ?&#x2018;˘đ?&#x2018;&#x203A;đ?&#x2018;&#x2013;đ?&#x2018;Ą đ?&#x2018;?đ?&#x2018;&#x;đ?&#x2018;&#x153;đ?&#x2018;&#x2018;đ?&#x2018;˘đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x2018; đ?&#x2018;?đ?&#x2018;&#x2019;đ?&#x2018;&#x; đ?&#x2018;¤đ?&#x2018;&#x2019;đ?&#x2018;&#x2019;đ?&#x2018;&#x2DC; = 5000 = 0.25 hours/unit Another example: a company generated ÂŁ80,000 worth of goods in 1500 hours. Labour productivity =

đ?&#x2018;Ąđ?&#x2018;&#x153;đ?&#x2018;Ąđ?&#x2018;&#x17D;đ?&#x2018;&#x2122; đ?&#x2018;&#x153;đ?&#x2018;˘đ?&#x2018;Ąđ?&#x2018;?đ?&#x2018;˘đ?&#x2018;Ą đ?&#x2018;Ąđ?&#x2018;&#x153;đ?&#x2018;Ąđ?&#x2018;&#x17D;đ?&#x2018;&#x2122; đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;?đ?&#x2018;˘đ?&#x2018;Ą

80,000 1,500

= ÂŁ53 per hour of work

The company generated ÂŁ80,000 worth of goods with 30 employees. Labour productivity =

80,000

= ÂŁ2,666 per employee a week

This equation works well if every person has the same job, producing the same product, in a factory with the same conditions; but the reality is far more complicated with a range of confounding factors. Measuring output by the worker is a complicated process that occupies

the time of many of the best worldâ&#x20AC;&#x2122;s economists. Debate rages on regarding the effectiveness of productivity measures and whether low productivity should even be considered a negative.

2.4 FACTORS THAT AFFECT PRODUCTIVITY ON CONSTRUCTION SITES: The conceptual framework shows how productivity (dependent variables) is affected by six independent variables.

Figure 1 factors that influence productivity on construction sites (Author, 2014) The above can be stated mathematically, according to Lamka et al. (2014) as: P= PP+E1+E2+WM+S+MÂąe Where: P= productivity PP= Project Performance E1= Effectiveness 19

E2= Efficiency WM= Work Measurement S= Strategy M= Management e= is an error margin of other unmentioned variables

2.5 WHAT ARE THE CHALLENGES THAT ARE FACING PRODUCTIVITY IN THE UK CONSTRUCTION INDUSTRY? The UK construction industry has always faced challenges in increasing productivity over the last decades. There are three current challenges facing the UK construction sector today, focusing on human, economic and cultural factors.

Chart 2 Comparing productivity growth between construction and the whole economy (Source: ONS) 20

2.5.1 Human factors: An ageing workforce, poor image of construction and lack of diversity. It is estimated that 22% of the workforce in the UK are aged between 50-60, and many will soon retire, which mean the UK construction workforce is ageing, and this is due to low interest in the construction industry for young people and due to the poor public image of construction. Furthermore, the industry is failing to promote careers in construction to young people and school-leavers (site diary, 2019). Studies predict that many millennials will possess little to no experience or interest in the construction industry, which if true, would pose a significant threat to future improvements in productivity and output. Increasing project complexity and an expected decrease in experience levels of the UK workforce is going to pose a significant risk where future project performance improvements are concerned. (Amy Haddow, 2019)

Research has shown that many young people and students who leave school in the UK have professed a greater interest in jobs relating to social media technology, such as being a 'YouTuber' or social media influencer, rather than more traditional or vocational careers. This trend could have a detrimental impact on their skill base. A survey from travel company First Choice of 1,000 children, age 6 to 17, reported that more than a third of the children want a career in the online video industry, YouTube especially (Rachel Hall, 2018) Furthermore, while more than 85% of apprentices in the construction industry find work, the sector is failing to deliver attractive career opportunities, especially for women, which will increase the labour shortage in the industry with time. (site diary, 2019)

Liz Waters and Sir Robert McAlpine did research when they investigated several young students, asking them what they want to do when they grow up, and the majority chose the 21

sport and entertainment-related careers. Sir Robert Mcalpine think this is happening due to celebrities and footballer influence on young people, especially using social media, which make the information easy to access. â&#x20AC;&#x153;Indeed, these professions are high profile and the rewards they bring highly visible. Maybe construction should take a leaf out of this book and look at more mainstream and modern ways of promoting itself to young peopleâ&#x20AC;? (Sir Robert McAlpine, 2016)

Modules students participate in

Health, Public services and care

6% 16%

Buisness administration and law

31%

Retail and commercial

16%

Engineering and manufacturing

31%

construction, planning and the built environment

Chart 3: Most trending modules for students in the 2016-2017 academic year (source: House of Commons library)

2.5.2 Economic factor: Brexit Most of the construction firms in the UK rely heavily on skilled migrant labour. The uncertainty created by Brexit and possible consequences of a no-deal may mean the construction companies will not be allowed to bring skilled worker from the EU, which will decrease productivity even more in the construction industry in the UK. As a result,

construction companies need to consider alternatives that will lead to the formation of technological solutions and new construction methods to replace EU, skilled workers. (Keith Harrison, 2019) Another problem related to Brexit is a lower pound rate. Since the Brexit voting, sterling has weakened generally, which mean that the imported construction materials will become more expensive. Low Currency rate will lead to rising construction costs and have a detrimental impact on productivity.

16 to 24 years

25 to 34 years

35 to 44 years

45 to 54 years

55 and over 0

Non-UK

Chart 4: % of those working in the industry by age (Source: ONS) The chart shows that the UK nationals working in the construction industry tend to be older, while the young labour is mostly Non-UK worker. Roughly 10% of the 2.2 million people working in the construction industry in the UK are non-UK nationals. (source: ONS)

2.5.3 Cultural factors: inefficiencies due to the prevailing â&#x20AC;&#x153;vicious productivity cycle.â&#x20AC;?

Figure 2: The Vicious circle Delivering the project on time and budget has been challenging for a while for most of the construction companies, and the Vicious productivity circle explains why. The challenges in tracking vital time, cost data and information relating to workforce, resources (equipment and materials) used in a project through multiple independent channels is only exacerbated by the presence of an uncollaborative, un-joined up industry culture. In this sort of context, information is not well-managed, stored or seamlessly exchanged between stakeholders and errors, meaning omissions and duplication of tasks are all too common. Inadequate and insufficient training in the construction industry and a lack of research and development leads to reduced employee fitness and performance on construction sites. The

leak of training makes employees more likely to seek alternative work or even lead to their dismissal. In turn, more money must be spent on hiring and training while less can be spent on wages and benefits that will lead eventually to low productivity in the construction industry.

2.6 WHAT IS THE INTERNET OF THINGS? Internet of things, known as (IoT) refers to billions of devices around the world that are now connected through a network enabling them to collect and share data. Using processors and nanotechnology anything can become a part of IoT; from pills to rockets to self-driving cars. IoT lets the devices connect and share real-time data without involving a person; in other words, it merges the digital and physical world. The internet (which is a significant component of the IoT) started as part of DARPA (Defence Advanced Research Project Agency) in 1962 and evolved into ARPANET in 1969 and after 11 years, the commercial service providers began supporting public use of APRANE, allowing it to evolve to our modern internet (Keith D. Foote, 2016).

2.7 HOW DID THE IOT START? IoT is connecting multiple devices and enabling them to share data. In the 1980s in the Carnegie Mellon University, Pennsylvania, a Computer Science graduate student named David Nichols was in his office on campus, craving a soda. His office was far away from the drinks machine, and he had repeated experiences of reaching the machine and finding it empty or containing only warm drinks as it had recently been refilled. He had an idea to help find out the content of the drinks machine remotely and put an end to unsatisfying soda runs once and for all. Soon, he told his friends about his idea ( Mike Kazar and Ivor Durham), and 25

John Zsarnay, a research engineer at the university. They started working with him to make his idea happen. The coca-cola machine worked in a simple way. When someone bought a drink, a red light would illuminate for a few seconds and then turn off, and once it was empty, the red light would stay on. In order to pull the data from the machine, Zsarnay installed a sensor that can sense the status of the light and a network line was connected between the machine and the central computer that was connected to ARPANET (a precursor to todayâ&#x20AC;&#x2122;s internet).

Kazar wrote a program for the gateway that checked the status of each column's light a few times per second. If the light turned off then on then off again, the program would know that a drink had been purchased, but if it stayed on more than 5 seconds, it assumed the column was empty. Also, the program detected how many minutes the bottles had been in the machine after restocking. After three hours, it notified that the drink was available to purchase as it was then cold; this was the first IOT machine. (Resource: IBM, Jordan Teicher, 2018)

2.8 WHAT ARE THE BENEFITS OF CONNECTING DEVICES WITH EACH OTHER WITHOUT INVOLVING HUMANS? IoT devices can be as safe as children's toys or as dangerous as a self-driving truck. The main reason for connecting devices with each other is to make life easier and more secure. For example, a jet engine contains thousands of sensors that collect and share data to ensure itâ&#x20AC;&#x2122;s operating in the right way to prevent any accidents, as well as smart cities that connect millions of sensors that can track people movement and help understand and control the environment more which will lead to a safe and clean cities (Steve Ranger, 2018). IoT

devices are attracting more attention and creating a new, integrated lifestyle for people. For example, a fitness tracker and smartwatch can track the wearer health and sports lifestyle and motivate him to exercise more and keep his/her heart rate monitored. A smart coffee machine can make coffee ready once the person wakes up. A smart and self-driving car that can detect the driver tiredness level and reduce the risk of accident from happening. (Margaret Rouse, 2019) Internet of things automates tasks that waste human hours; this is where technology has enormous implications for construction.

2.9 HOW BIG IS THE IOT? There are already more connected devices than people in the world, that is how big the IoT is, and it is getting bigger. It was estimated at around 8.4 billion devices in 2017 with the expectation of reaching 20 Billion in 2020. According to Gartner (an IT service management), most of the IoT devices are smart TVs, security cameras and small electric meters. (Shaping Tomorrow, 2019) Table 1: IoT units installed base by category (Millions of units). Source (Gartner, 2017) Category

2016

2017

2018

2019

Consumer

3,963.0

5,244.3

7,036.3

12,863.0

Business:

1,102.1

1,501.0

2,132.6

4,381.4

1,316.6

1,635.4

2,027.7

3,171.0

6,381.8

8,380.6

11,196.6

20,415.4

cross-industry Business: verticalspecific Grand total

2.10 WHAT IS IOT FOR CONSTRUCTION? IoT links physical devices such as power tools, safety helmets, working shoes or a compressor with digital information and processes using sensors. The data can be captured automatically in the construction sites and sent to software like BIM that can help the construction companies to run and track the necessary business process (Hilti, 2018)

2.11 WHAT IS ARTIFICIAL INTELLIGENCE (AI) AND HOW IT IS RELATED TO THE IOT? Artificial intelligence is the simulation of human intelligence processes by machine, especially computer systems. It lets the machine learn, act and self-correct using past programmed information or real-time data processes (Nick Heath, 2018) AI can be classified into two categories, weak AI and strong AI. Weak AI is designed and programmed for particular tasks, such as personal assistants like Siri for Apple and Alexa for Amazon, while strong AI (known as artificial general intelligence) can have abilities comparable to that of human intelligence, and it can surpass it if it was programmed to do so. It can find solutions and solve problems without the interference of humans. Use of AI machines raises ethical questions, but the machines are only as smart as the data a human gives them during training. For example, a smart robot is programmed to do a task that is hard for a human to do, such as the robots that Nasa sends to space and those used in car manufacturing (Certes, 2018). AI uses IoT to collect data, learn and connect with other devices. For example, a self-driving car uses a combination of many sensors. It has cameras with image recognition and visual motion that use AI for deep learning to build an autonomous skill that makes it able to drive the car by itself while staying on the right lane, understanding the road sign and speed limits

and avoid any obstructions such as pedestrians or road closures. (Michael Hicks, Michelle Fitzsimmons, 2019)

2.12 HOW CAN IOT AND AI UNLOCK THE PRODUCTIVITY ON THE CONSTRUCTION SITE? Internet of things known as (IoT) and artificial intelligence (AI) has been a trend lately, especially in smart homes and smart cities (Devin Pickell, 2018), but can this topic unlock the productivity growth on construction sites? Improving maintenance and mechanical repairs are just some of the ways it can contribute, in addition to tracking assets and making sure they never get lost or stolen, which frequently happens in large-scale construction sites. Labour productivity growth in construction has averaged only 1% a year in the past decades, which is the lowest productivity growth in all sectors. When this is compared with 2.8% growth for the global economy, it emphasises the importance of finding new ways to improve productivity. (Filipe Barbosa et al., 2017) IoT can be the key to unlock productivity growth in many ways and transform the way firms look at managing their builds. One of the ways is by ensuring that construction companies have access to real-time data. This data can track machine performance and undertake preventative maintenance, which will reduce the downtime and repair costs plus the time for locating assets. (Nick Hertzman, 2017) IoT can help prevent critical heavy equipment from going out of commission and hindering the progress of all the other trades at work. Sensors can be applied to heavy construction equipment, enabling the remote monitoring of excessive vibrations, temperature fluctuations, and other potential maintenance problems. When the sensors detect abnormal patterns, they issue alerts that can trigger maintenance workers to investigate and correct the problem 29

before critical equipment fails. This type of predictive maintenance can save time and money and protect construction projects from unnecessary delays. (CRL, 2018)

2.12.1 Using Machine control and autonomous construction equipment Machine control is the first step to self-driving construction equipment on construction sites, which can reduce delays and save money. The self-driving construction machine will use a variety of measurement method such as LIDAR (A surveying method that measures distance to an object by using laser light and sensors for reflecting light) and GNSS (Global navigation satellite system) to automatically adjust heavy equipment to be able to drill, grade, excavate or pile large areas. The machine will have higher precision than a human, their progress and movement will be reported in real-time, and it will increase productivity and reduce delays. (Matt Alderton, 2018) Autonomous construction equipment increases safety on site by protecting the operator. Operators historically have had tough jobs; they are driving something off-road, which hurts their necks, backs and make their hands tired, plus most construction and site preparations are happening at night, so operators take little micro naps while they are working which increase the risk of incidents according to Van Hampton (Carmen Reinicke, 2018). Using autonomous vehicle have economic value because it increases productivity. Canadian oil company Suncor Energy is deploying self-driving haulier trucks in Albertaâ&#x20AC;&#x2122;s oil sands to increase productivity by eliminating human error and increase haulier truck trips per day, which improve the total production output (Dan Healing, 2018). Furthermore, implanting IoT in the construction vehicle will make their life longer, using sensors that monitor vehicle performance, such as trucks and excavators, and send real-time

data about the fuel consumption, location, speed, engine temperature and maintenance requirements lead to better efficiency. Using these sensors will decrease the chance of machine errors and will save time fixing or replacing them. It will also increase safety on site by monitoring the driving performance, which will lead to an increase in productivity (Unearth, 2018). Nevertheless, smart autonomous machines can work and do the task by its own by plugging a three-dimensional terrain model into the machine, which let the machine optimise to the grades that are specified in the model (Matt Alderton, 2018). Autonomous machines will not lead to the extinction of operators; Human is still needed to make the right inputs and fix errors (Van Hampton, 2015). A company called Build-robotics, founded in 2016 in San Francisco California, is building robots to make construction safer, faster and more productive. Furthermore, as we said before, sensors can enable machinery to detect maintenance requirements and detect errors early that can help prevent severe and expensive damages, and also it increases efficiency by monitoring fuel consumption. This technique help construction company to know the time machinery can work for and maintenance time to prevent damage, and in this case, it boosts the site productivity. (Mohsen Mohseninia, 2018) 2.12.2 Using Site monitoring sensors IoT can help track workers on construction site to ensure they are doing their tasks and can track materials and equipment to make sure they never get lost or stolen, which can cause delays and decrease productivity, especially in large-scale construction sites (Mohsen Mohseninia, 2018)

The project manager will be able to prevent an accident from happening and prevent any violation of safety standards using site monitoring, which will reduce delays and incident costs and increase productivity on site. (Jenny Clavero, 2018) To be able to monitor construction sites, sensors should be put all around the site that can track worker movement, performance, detect noises, vibrations, radiation and hazardous materials, temperature, humidity and continually collect, report and analyse data depending on the condition they are programmed to measure. (Unearth, 2017) Pillar Technologies is a company developing smart sensors designed to be installed in the construction sites, that can detect temperature, humidity, dust particulates, pressure, noise vibration and volatile organic compounds which arise from an overload of varnish or paint. These sensors provide real-time data analysis for the site manager and give real-time alerts, such as rising temperature or humidity in a specific area. This data helps the site managers to mitigate the problem before it escalates and prevent damage before it occurs. Furthermore, this sensor can analyse the data over time, helping the contractor to understand the site better and track job performance over the project life cycle (Fitz Tepper, 2017).

2.12.3 Using Smart personal protective equipment (PPE) and wearable technology onsite Smart PPE allows the site managers to track worker location and to see if they are in danger, which will save time and increase compliance. Smart PPE will result in improved worker protection, comfort, health and safety. Consequently, using real-time data can create a happier workforce.

Also, sending the worker performance data and tracking incident using PPE will help to identify a better solution in the future and help prevent the same incident from happening using the AI. Compared to traditional PPE, smart PPEâ&#x20AC;&#x2122;s advanced features heighten usability and boost efficiency. Smart PPE leads to a reduction in errors, and therefore, the number and severity of workplace accidents and injuries, giving rise to improved productivity, performance and efficiency and consequent long-term cost savings. (Source: Hazardex, July 2018) There are five types of PPE in construction site: eyewear, helmet, gloves, safety boots and high visibility clothing (workwear). â&#x17E;˘ Smart eyewear: Intelligent glasses can provide data and enhance the communication between workers, which can give them all the details they need on the side of the lens, and it can also warn them when they enter the hazardous area. The smart glasses are connected with sensors and other devices that will provide information visually using the lens screen rather than audibly. Using the screen is safer in case the worker is working in a noisy environment that makes it harder for them to listen to an emergency warning or information using traditional ways such as radio (Walkie Talkie). (Hazardex, 2018) For example, Iristick made three types of smart glasses that feel and look like regular glasses but have a central camera for a realistic, unrestricted human perspective. They are also voice control enabled, which will let the worker talk and send the live image to the site manager using AR and also an optical zoom lens to focus on operation details plus a heads-up display to share information and give instructions to the wearer. This smart glass can give the site manager a closer and more detailed view of what is happening on the site, which leads to better risk managing. (Iristick, 2019) 33

AR will affect companies in every industry and many other types of organisations, from universities to social enterprise (Iristick, 2018). Micheal Porter, a University Professor at Harvard business school, said that â&#x20AC;&#x153;In the coming months and years, AR will transform how we learn, make decisions, and interact with the physical world. It will also change how enterprises serve customers, train employees, design and create products, and manage their value chains, and ultimately, how they compete.â&#x20AC;? (Christiana Polycarpou, 2017) â&#x17E;˘ Smart helmet: Smart helmet use sensors to evaluate situations and automatically enhance the protection function. Sensors and inside chips can be programmed to detect anything, for example, a smart helmet can detect if the worker will have a brain stroke (ischaemic) caused by clot limiting blood flow, this technique is being used in some construction companies in Australia, which has a high percentage of ischaemic stroke. (Malek Murison, 2016) Furthermore, a smart helmet can be programmed to detect hazardous places that will give the worker a warning such as vibration or sound to warn them that they are approaching a dangerous area, it can also detect falls and send a notification to the site manager. Also, it can be provided with GPS tracking system for lone workers in big construction sites where there are many workers and equipment and keep informing the site managers where the workers are and if they are in the right places. (Business, 2017) A company called Wakecap is producing smart construction helmet that use the IoT to improve safety and workflow on the construction sites. The electronic component is equipped with a buzzer and a panic button to facilitate two-way alert system between the workers and a safety manager. In case of an emergency incident, each worker is simultaneously alerted with a loud buzzer, as soon as a worker hears a sound, he presses the button on the knob of the helmet to acknowledge and notify that, he heard the alarm. He then moves to a safe zone. The 34

safety officer has access to a dashboard, where he can check who is still stuck at the site in real-time and efficiently improve the response time towards a worker who is still stuck. Since the location of each worker is known, the site manager can administer a faster response and an effective evacuation. (Ishita Kochhar, 2019) ➢ Smart safety gloves: NFC chips can be installed into the safety gloves, and customised with other machines, that will let the worker able to use the machine only if he is wearing the right gloves. The gloves can contain chips to know if the worker is wearing them, and they can also detect hazardous materials upon touching them. These gloves can also work with other PPE such as smart boots using the IoT and make them communicate with each other and prevent the worker from entering a dangerous area if they are not wearing the right PPE. These smart gloves will prevent accidents and eliminate human errors. (Hazardex, 2018) ➢ Smart safety boots: Intelligent boots use smart orthotic insoles with sensors installed into them that can detect and evaluate risk such as slipping or falling and warn the wearer and other workers to prevent any accident. (Intellinium, 2018) Smart boots can also detect if the wearer is wearing them correctly or if they are damaged, and they can be connected to smartphones and other PPE for data sharing. ‘’Digital technologies like sensors ...may be used to detect proximity to hazardous zones and the risk of fatigue, alerting a field worker and the associated supervisor. Radio-frequency identification (RFID) can be used to identify workers when they enter a plant and automatically assign tasks to them over mobile devices.” (DR. Sanjoy Paul, Global Manufacturing)

As has already been mentioned, sensors can be programmed to detect a wide range of inputs. For example, smart boots can be programmed to measure tiredness by evaluating the way the worker walks and notifies the wearer when they need to rest or stop working. These sensors will enhance safety and prevent accidents from happening as most are due to tiredness and worker fatigue. The same technique is used by some cars which can detect if the driver is sleepy or tired by the way they drive and notify them that they may need to rest. GPS can also be installed in the smart boots that can track worker movement in big construction sites, and notify if they are in places they are not supposed to be. â&#x20AC;&#x153;Wearable devices are gaining ground, especially for workers in outdoor, hazardous environments where detecting falls or pinpointing location could save lives. Barriers to be overcome include device battery life and back end infrastructure to process the information into insights; There remains a compelling case for wearables that reduce risk and improve the safety of workers.â&#x20AC;? (Financial Times, June 2016) â&#x17E;˘ Smart workwear Sensors can be installed on workwear that can detect dangerous noises and can measure heart rate and body temperature. This data will be shared using the IoT with other devices and with the site managers that will enhance safety by tracking the working environment and managing risks before they escalate. (Hazardex, 2018) Snickers workwear is one of the new companies using this sensor in workwear clothes. 2.12.4 Using Building Information Modelling (BIM) BIM is a process that delivers a 3D model of a building, and it is often used during the design and construction phases. IoT sensors can be integrated into a model, creating data that can be used to model occupant movements, temperature trends, and energy usage patterns. The 36

output can be examined to improve projects in the future and even improve building operations management. (CRL, 2018) Adding to that, BIM offer 4D as Time, 5D as Cost and 6D as-built operation. In other words, BIM is not a simple geometry. It takes additional features into accounts, such as light analysis, spatial relations, and geographic details, as well as details on building components, cost. (Designingbuilding, 2018) BIM can decrease several errors during the design phase, which lead to fewer mistakes on construction sites. BIM also offers the ability to work simultaneously, which help save time. The works can be in progress while at the same time, the project team can work on drawings and estimations, diagrams and other aspects. Furthermore, BIM allows doing more work with a smaller team, plus give the team better collaboration and communication. Using BIM can increase quality and save time in the construction industry. It can also improve safety on construction sites by detecting the hazards that may become problems during design, which help to avoid physical risk by planning site logistics ahead of time. (John Hall, 2018) 2.12.5 Using Intelligent prefab Prefabricated concrete is a modern way of building and using it can be faster and more costeffective with less construction waste than traditional building methods. However, using prefabricated concrete for a large project can be very complex to coordinate, and this is where IoT can help to solve this problem. (CRL,2018) Radio Frequency identification sensors (RFID) can be used to track individual prefab through the supply chain. (Case study: construction of Leadenhall Building in London used RFID to help mitigate the effects of any downstream delays in the construction, especially during the

installation of the prefabricated components, which was extremely complex due to the location of the building and the small space area around it. The data was fed into BIM once parts were installed, allowing for real-time rendering of the building in progress, as well as the establishment of project controls and Key performance indicators). (Mark Roberti, 2013) 2.12.6 Using Augmentation Reality for Better Visualization Intelligent Prefab using IoT helps to connect the dots in real-time. With augmented reality, architects can share design models and layouts to Prefab suppliers to have a better grasp of the project even before the building starts. Innovative VR Company Viz360 creates VR tours and 3D model viewers based on floor plans or 3D CAD models. (DesignBuild, 2019) Furthermore, a company called Daqri is developing a smart protective helmet that uses Augmentation reality technology. The Smart Helmet is capable of visualising projects and 3D models in augmented reality as an immersive and large-scale 3D environment. Teams can compare work-in-progress with the original design and keep the work and office in sync with an all-digital workflow (Eduardo Souza, 2019). The helmet comes with a transparent AR screen that works as safety glasses at the same time; the screen provides real-time data for the worker using BIM, allowing the construction worker to share and view various building elements, data and plans. Also, the helmet contains cameras that can capture and display information about the user environment. (UKconstruction, 2016) Daqri helmet will guide the worker and instruct by providing augmented work information in real-time, helping workers to understand processes using the AR screen. It will also provide a 3D reconstruction of the building showing the workers how the project should look like when it is complete, which will reduce errors and time spent on site (Skanska UK, 2017)

2.12.7 Using concrete sensors DOKA Concremote is a real-time information system that helps in taking decisions at the building site and in the concrete factory (Concrefy, 2018). The system has gained multiple awards across the globe, including the most recent Construction News Awards for best commercial innovation of the year in the UK. Concremote uses GSM enabled digital sensors to measure the inset concrete maturity (temperature x time) gradient, and with this data, it calibrates early age strength (Doka UK, 2018). Concremote can control production equipment, open casings mechanically, direct climate controls rooms and operate cooling and heating containers on location. Concremote also communicates with BIM, existing (Enterprise Resource Planning) ERP systems or control modules. In turn, using Doka Concremote improves construction processes and boosts productivity. (case studies: Nakheel mall, Dubai, build with Concremote) Using Robots to boost productivity A Wembley Project case study provides information that the contractor Sisk is set to become the later contractor to embrace robotic technology and is planning to trial block laying using robots at Wembley park project, where it is building 743 home to rent (Source: Construction manager magazine, 2019) The contractor is also now tagging assets for BIM as it plans to expand its build offer to include five-year maintenance services. â&#x20AC;&#x153;This will involve fitting sensors to buildings to make them cognitive so we can closely monitor when they are feeling sick or breaking downâ&#x20AC;? (Bowcott, 2019). Manufacturers are trying to focus on creating interfaces that are easy to use to make robots accessible to program, which make it easy for innovators to implement robots in the

construction sectors. There is a potential to link robots with digital models, which mean they do not need to get programmed by a human. (Construction manager magazine, 2019) “The current focus for Skanska is connecting the CAD systems to robots, so the robot can understand and have the autonomy to execute tasks without the need of a human” (Felipe Manzatucci, 2019). “The right data capture from the CAD systems will be an enabler for robotics artificial intelligence, an important step in making digitalisation an enabler of industrialisation.” 2.12.8 Artificial Intelligence Will Make Jobsites More Productive Combining AI with IoT brings the promise of a new future (Abhinav Shrivastava, 2019). Several companies have started offering self-driving construction machinery that uses artificial intelligence to perform repetitive tasks such as pouring concrete, welding and demolition, such as Build Robotics company. Also, excavation and pre-construction work can be done with autonomous or semi-autonomous bulldozers that can prepare the construction site in exact specifications using a program written by a human, which can reduce the overall time required to prepare the site and complete the project (Lior ZitZman, 2018). Project Managers can also track job site work in real-time using facial recognition, cameras and sensors to assess worker productivity and conformance to procedures (Highway Industry, 2019) Artificial Intelligence Will Address Labour Shortages A 2017 McKinsey Report says that real-time data in the construction firms could boost productivity by as much as 50%. Many construction companies are using artificial intelligence and machine learning to enhance their planning for the distribution of workers and machines on construction sites.

Using AI in robots can help evaluate the progress on the construction sites regularly. It will also update the location of workers and the equipment which will help the project managers to know instantly which areas have enough workers and equipment to complete the job on time and which need more labour to prevent delays. (Sumana Rao, 2019)

2.12.9 Artificial Intelligence can manage a whole project The project manager should consider many factors while managing a project, and each factor can impact the project in a negative way and cause delays. AI can manage the whole project while providing the builders with all the potential risk, constructability and the structural stability of various technical solutions for all kind of projects (Donovan Alexander, 2019)

2.13 SIDE EFFECTS OF THE INTERNET OF THINGS AND THE POSSIBLE RISK Security leak and cyber-attack is a significant risk of IoT. Attack on IoT devices will lead to a critical side effect, that can cause problems and severe damage, especially that everything will be connected through the clouds (David Roe, 2019) IoT needs reliable connectivity to be able to disrupt the industry and make changes if an IoT device lost connection and stop being able to send and receive data, it will lose the ability to track assets and construction equipment and vehicles on the sites which will create a blind job site. For example, if the company use EE for connectivity and the EE company experienced a large scale black-out like it happen in 2017, then the devices will not be able to transmit data, which mean that the construction company that uses this connectivity will be left clueless on what happening on site (Francesca Gillet, 2017)

2.14 HOW IOT SECURITY AND CONNECTIVITY CHALLENGES CAN BE MITIGATED? Mesh network technology (wireless connection through beacons) can offer stable connection without relying on power, GPS or internet (Ishita Kochhar, 2019). Also, a multi-network roaming SIM (subscriber identify module) can switch between providers who ensure more reliable connectivity, but the gap time between changing provider is a blind time. The latest version of WIFI is made for IoT, which is called WIFI 6 (802.11ax) have the ability to connected 100 of devices at once while keeping the excellent connectivity between them and provide more than 10 times the current speed of WIFI 5 (Ruckus Network, 2019). Furthermore, the 5G network is expected to solve all the IoT connectivity challenges, which is 10 times faster than the current 4G network and have more stable connectivity with other benefits (Devin Pickell, 2018). Nevertheless, scanning the network to identify all the connected devices incorporate security by design and use hidden network and VPN is one of the methods to prevent cyberattack on the IoT devices (CIPHER, 2017)

2.15 CHALLENGES FACING THE IMPLEMENTATION OF IOT AND AI IN THE CONSTRUCTION INDUSTRY IoT is moving full steam ahead, changing the world we know, and it can change how construction sites work. However, there are a few challenges and barriers to adopting IoT and AI in the construction industries. Brian Buntz from IoT world today did a survey about why people are not embracing the Internet of Things, and the survey showed that most people are worried about data privacy and security in case the devices get hacked and a small number of participant think that the IoT technology is not sufficiently mature to be used yet.

The chart below summarises the top barriers to the implementation of IoT.

technology is not mature current workflows not well defined uncertainty that the IoT will deliver the benefits promised interoperability concerns lack of standards inadequate infrastructure Not enough knowledge about available solutions High cost of implementation

Security concern Data privacy concern 0%

10% 15% 20% 25% 30% 35% 40% 45%

% of participant

Chart 5: Reasons people are not embracing IoT (source: Brian Buntz, 2016)

2.16 THE USE OF THE INTERNET OF THINGS IN OTHER INDUSTRIES IoT is being used in many other industries, especially in healthcare. The IoT has opened new possibilities in medicine, especially in collecting patient data using IoT medical devices, that can give extra insight on symptoms and help patients to track their health on timely bases (Econsultancy, 2019) With the Help of IoT, people can know to monitor their glucose level using smart continuous glucose monitor (CGM) that send the patient glucose data to a smartphone, allowing the patient to check glucose level and detect symptoms. Furthermore, a company called Verily is developing smart contact lenses (glucose-sensing lens) that can detect tear glucose and use

the IoT technology to send data and provide an early warning system for people with diabetes to alert them when their blood glucose level has risen or dropped (Brian Otis, 2018)

2.17 CONCLUSION Labour productivity growth in the construction industry has increased by 1% a year over the past two decades, while the entire world economy has a 2.8% growth and 3.6% in manufacturing, according to research from the Mckinsey Global Institute. (Mohsen Mohsenina, 2018) Nonetheless, if productivity growth in the construction sector can match the productivity growth of the total economy, it would boost the sectorâ&#x20AC;&#x2122;s value. The research shows that other industry such as healthcare is implementing the IoT technology in the medical sector to make it better and save more people, which is a proof of how vital the IoT is to improve productivity and make things better. This literature review proves that Artificial intelligence Robots and the Internet of Things can reduce the building cost and time while keeping the excellent quality. However, despite the prediction of massive job losses, AI is unlikely to replace the human workforce; instead, it will improve the productivity in the construction industry by reducing expensive errors, worksite injuries and make building operations more efficient. The literature review revealed the side effect and the barriers of the application of IoT in the construction industry, which may lead to serious damage if it happens. Nevertheless, the review revealed some possible mitigation regarding the connection and security risk.

3 CHAPTER THREE: RESEARCH METHODOLOGY, DATA COLLECTION AND DATA ANALYSIS 3.1 INTRODUCTION This chapter describes the strategy and method used in collecting, processing and analysing the required data that will address the research questions appropriately. The research study seeks to address the productivity challenges facing the UK construction industry and analyse how to unlock the productivity growth in it using the Internet of Things and Artificial Intelligence technology. It will also identify the barriers for the application of such technology in the construction industry. The strategy and methods used are the key to access quality data in addressing the research problem.

3.2 RESEARCH PHILOSOPHY Research philosophy is a belief about how the phenomenon of data should be gathered, analysed and used (Galliers, 1991). According to Fellows and Liu (2015 page 69), to create a research philosophy introduces the principles that guide the process in extending knowledge and seeking solutions to the research problems. It is stated that the choice of research philosophy is mostly determined by the research problem (Pranas Zukauskas, 2017). Furthermore, research philosophy is defined as the development of knowledge and the nature of knowledge (Saunders et al., 2009). Understanding the issues of the research philosophy before commencing a project is essential to know how to conduct the research (Crosson, 2003). The rationale behind the choice of approach is the research question, using qualitative or quantitative approaches will not wholly address the research problem, but the combination of both approaches does (Creswell & Plano Clark, 2011). 45

The research philosophy used is the research onion process (Saunders, 2009)

Figure 3 the research Onion process (Saunders, 2009) The onion peel is used to identify the research approach to this study. The onion contains several layers, starting with the outer layer which comprises the philosophy of the research, then to the inner layer of approach and the methodological choice layer. After that, the strategies used layer followed by the data collection methods layer. This scenario gives a detail operational view of the type of philosophy adopted. â&#x17E;˘ Positivism Positivists believe that reality is stable and can be observed and described from an objective viewpoint without interfering with the phenomena being studied (Levin, 1988). Using positivist research result in a more reliable way to find data because it is not influenced by the unpredictable behaviour of humans (Biggam, page 168, 2015). The emphasis on quantifiable data is the purpose that positivist research is associated with quantitative research (Ibid, 2015) 46

â&#x17E;˘ Interpretivism In interpretivism research, the participant's experience in the research field of study will affect the participant's view of the situation being studied (Creswell et al., 2003). Interpretivism is the antonym of positivism; it uses human participation and observation. The research to be identified as qualitative research (Biggam, page 168, 2015). It is suitable to use the interpretivism philosophy in this study since the project managers in the construction industry have different views addressing the productivity problem. â&#x17E;˘ Pragmatism Pragmatism uses both positivism and interpretivism research methods; it is a mix of quantitative and qualitative data collection. The speciality of pragmatic research philosophy is that it places the research problem in the middle and applies all the methods to understand the problem (Creswell et al., 2003). The table below explains the method used and the data collection tools in each research philosophy (source: Mackenzie & Sally, 2006)

Table 2 Data collection methods of different Research philosophy Research philosophy

Methods

Data collection tools

Positivism

Quantitative as a primary

Questionnaire

method and in some cases

Experiments

Qualitative methods may be used Interpretivism

Qualitative as a primary

Interviews and survey

method and in some cases,

Visual data analysis

Quantitative methods can be used. Pragmatism

A mix between Qualitative

A mix of positivism and

and Quantitative methods

interpretivism collection

used together

tools

3.3 RESEARCH STRATEGY Research strategy can be defined as how the research objectives can be questioned (Shamil Naoum, 2013). There are two types of research strategy, either quantitative research or qualitative research. The quantitative research is “objective” in nature. Creswell (2014) describe quantitative research as an inquiry into a social or human problem, based on testing a hypothesis or a theory composed of variables, measured with numbers and analysed with statistical procedures in order to see if the hypothesis or the theory holds true (Shamil Naoum, 2013). This method is used to quantify opinions, behaviours and generalise results from a large sample population using questionnaire surveys (Susan DeFranzo, 2011). The qualitative research is “subjective” in nature. It is used to gain an understanding of underlying reasons, opinions, and motivations (Susan DeFranzo, 2011). It emphasises meaning, experiences and description (Shamil Naoum, 2013) and provides insights into the problem. Qualitative research lies among two research categories - exploratory and attitudinal. Exploratory research is used when the researcher has limited knowledge about the

topic, while attitudinal research is used to evaluate opinion and view of the participant in a particular subject. Qualitative research and especially attitudinal research are adopted in this study because it will give an in-depth and detailed response with feedback from the project managers about the productivity problem they are facing and if they use any form of IoT in their construction sites to improve productivity.

3.4 DATA COLLECTION Concerning the qualitative research strategy adopted in this study, the methods for collecting primary data will mainly be through interviews with several project managers, while secondary data will be collected using journals, articles, publication and other critical stakeholders in the construction industry. The reason why the questionnaire data collection method is not adopted in this research is that personal interviews can provide more information about the subject answers while providing the same sort of statistical precision. Interviews can be more useful than questionnaires, especially in this study, and that is because the researcher will be able to collect non-verbal data such as body language and can see if a particular question make the participant nervous or see if they struggle to answer a question. In other words, an interview can provide information that cannot be collected from a written questionnaire. For example, lack of eye contact, defensive posturing or hand signs can provide context to an intervieweeâ&#x20AC;&#x2122;s answer (Anna Green, 2017). Furthermore, since questionnaires take place without the presence of the researcher, it is hard to know if the participant understands the questions. Whereas during interviews, the interviewee can ask for more detail in case he/she did not understand the question and the

interviewer can ask follow-up questions to get a more in-depth response. In this case, interviews can give more detailed data with better quality. Mathieu Deflem, a professor at the University of South Carolina, explains that interviews are a better tool than questionnaires in order to collect primary data. That is because the “interviewer is the central instrument of investigation” which mean that the discussion can bring up new issues or questions that can give valuable data for the study.

3.5 INTERVIEWS The interviews aim to collect data for the qualitative approach. The interview questions are structured and will help bring out the opinion of interviewees regarding the “What” and “How” questions of the research. The question is presented in the same order and with the same wording to all interviewees in the structured interview, in this technique the interviewer may start with “open” question but will soon move towards a “closed” question format (Shamil Naoum, 2013). The interview question focuses on the research study and allows the interviewer to ask more follow up and detailed questions during the interview, which provide an in-depth and dynamic response. The interviewees are project managers from different companies in two different countries (the United Kingdom and Lebanon). The reason why the interviews took place in two different countries is to be able to compare data and discuss different company ideas about using the IoT in the construction industry. The interview will take place in person as a face to face interview, and they will be recorded with the interviewee's authorisation, using voice memos application on a smartphone iPhone. If the interviewee is not comfortable with this approach, then notes will be taken during the

interview. The duration of the interview is estimated to last between 20 to 30 minutes, with twelve substantial questions.

3.6 DATA ANALYSIS Data analysis is the process of bringing order, structure and meaning to the collected data (Marchall and Rossman, 2014). The data will be described and later analysed to produce an empirical research finding. The primary data is collected using interviews. In order to analyse this qualitative data, it should first be organised in a way to make it easier for the researcher to go through each question and different interviewee answer. After that, the interviewee response will be categorised by identifying the words and phrases used frequently that will help the researcher to analyse the data. The ability to categorise and compare the responses with the literature review will help to validate the accuracy of the research findings.

3.7 ETHICAL CONSIDERATIONS Ethical considerations in research are essential. Ethics are the norms or standards of conduct that distinguish between right and wrong. They help the researcher to determine the acceptable and unacceptable behaviours during the interview. The economics and social research council state that research ethics indicate the moral principle guiding research, from collecting the research data until the publication of results and beyond (ESRC, 2013). In this research, ethical considerations have been set to a high level in order to protect the anonymity and confidentiality of all the participants.

The following steps were taken in order to protect the participants: 1. The purpose of the research will be apparent to all the participants, that it is a university masterâ&#x20AC;&#x2122;s degree research 2. The participants are informed that there will be no financial rewards for taking the interview 3. An approval from the participants will be asked in order to use the data collected during the interview 4. The participants are informed that their identity and contact number will be anonymised while writing the results 5. Authorisation from the participants will be taken before recording any data during the interview 6. Participants will receive an information sheet about the questions before the interview

3.8 SUMMARY This chapter explained the use of interpretivism research philosophy as the critical philosophic approach in this research. The interpretivism philosophy uses the qualitative data collection method, which collects the subjective view of the project managers using interviews. Data analysis will be made by organising and categorising the interviewee responses and then comparing them with the literature review. Consideration of the ethics in this research will be present throughout.

4 CHAPTER FOUR: INTERVIEW FINDING AND ANALYSIS 4.1 INTRODUCTION Several interviews were conducted in order to understand more clearly how the project managers in construction companies define productivity and to identify the problems facing productivity growth. The interviews also consider their point of view regarding IoT and AI in construction sites to improve productivity. This chapter will describe the findings of the interviews conducted with the project managers, followed by analysing the interview results.

4.2 INTERVIEW FINDINGS The research study interviewed five project managers from different companies (Osborne, McLaren construction, RF construction consultant, Khatib & Alami and ACC) three of them in the UK and two in Lebanon. All subjects are referred to as anonymously. The project managers in the UK are referred to as PM “A1”, PM “A2” and PM “A3” and the project managers in Lebanon are referred to as PM “B1” and PM “B2”. Four of the interviews were conducted in person, as face to face interviews and one conducted by telephone call. The interview is comprised of thirteen questions related to the productivity and the application of IoT and AI in the construction industry. All project managers received the same question in the same orders, and the structured interview questions are shown in the Appendix. The results of the interviews with the different project managers are transcribed and analysed below.

4.3 INTERVIEW RESULTS AND ANALYSIS The first question asked to the PMs focuses on the projects they are working on and when the deadlines are. Each PM talked about the project they are working on, and some of them are working on a different project. PM “A1” is working on student accommodation, and affordable housing project for a private developer and the deadline is on the 16th of September. PM “A2” is working on a new sports block project and the deadline is expected to be on the 10th of October, while PMs “A3, B1 and B2” are working on different projects. The interviewees agreed on defining productivity as the measurement of the amount of work produced per period depending on the resources received for that period. PM “B1” gave an example of productivity by saying “ If we set a time budget for a specific task as 1000 hours, if we finish it in 800 hours with the client requested quality, we gain 200 hours which mean the productivity is good, but if we finish it in 1200 hours, it means there is a lack in productivity”. When the interviewees were asked about the strategy they are using to increase productivity on the project site, each one answered differently. PM “A1” explained that making a pleasant working environment, creating meetings with the parties involved in the project to agree on the job program and sticking with it is the strategy used to increase productivity. PM “A1” added that the program mentions all the different process that should be used in the construction site in detail. Moreover, not changing the contract program is an excellent strategy not to waste time and money, and in that case, we can increase productivity. Using a full cast and resource program, setting a target and trying to reach it and making sure that the labourers have all the information they need to do their tasks ideally is the strategy PM “A2” uses to increase productivity. PM “A3” explained the importance of communication with the client and improving this communication and flow of information is the strategy they use to

increase productivity. PM “B1” explained that choosing the right workers who can work on a fast track and under pressure with past experience in similar projects is essential to increase the productivity on site. PM “B1” added that they track labourer's works weekly and do a result check on the end of each week to see if there is a lack in productivity. If there is, they try to improve it by doing meetings, changing the unsuitable labourers and creating a work breakdown structure in order to increase productivity. This is something PM “B2” agreed on. The above data reveals that all PMs agree that achieving good productivity on site is mostly driven by choosing the right labourers for the task and adhering to the work program structure. The three UK based PMs “A1, A2 and A3” indicated that the critical challenge facing productivity on construction sites is skilled labour shortage, breakdown in communication and the project performance. The skilled labour shortage makes it harder to find the right people to do a specific task, especially if there are other significant projects in the same area. PM “A1” explained how they are facing a challenge finding experienced labours due to other significant projects that offer a better salary for a worker in the same area. This leads the worker to leave to a better-paid job, and in this case, they lose experienced labourers. PMs “A2 and A3” also added that the breakdown in communication is due to many layers between the client and the site manager, making the flow of information slower and imprecise. This leads to delays, affects the quality and decreases the site productivity. PM “B1” proffered that they are facing two kinds of challenges; internal and external. The internal challenges are described as resource risks such as unskilled labour and technological risk; if new software is being utilised and the team is not well trained to use it, then it can cause problems that lead to low productivity. The external challenges are often described as client risk, sometimes the client asks for a design change or wants the task earlier, which will

affect the project life cycle. PM “B2” added that delayed material delivery and poor communication between workers on-site are the major productivity challenges they are facing. All interviewees were assured that the poor communication between workers, contractors and site managers is the most significant barrier in achieving good productivity in the construction sites. None of the UK based interviewees uses augmentation reality on their project site, while PM “B1” explained that they use AR in some projects to show the client virtually how the project will be. PMs “A1 and B1” both use the internet-connected cameras on the project site in order to let the client see what is happening on-site in real-time. PM “B1” added that they are planning to use drone technology which will give them faster decisions and better surveying. All the PMs are aware of the Internet of Things and Artificial Intelligence devices, but none of them uses it in their projects. PM “B1” added that they are planning to use IoT in their Enterprise Resource Planning (ERP) system. This will include digital information so they can monitor what is happening on the site with the help of IoT sensors. PM “A1” considers IoT and AI devices non-essential in non-complex projects, stating that they have all the details they need and can track workers on-site and know if they are working using fingerprint gate. PM “A2” disagrees, considering the fingerprint gate to be an inaccurate method to track workers, because the worker can be inside the site without doing their task. PMs “A2, A3 and B2” consider the reason for not adopting the IoT technology is the lack of knowledge from the company and not knowing the difference between cost and value of benefits. PMs “A2, A3 and B1” believe that using IoT and AI on construction site has the ability to increase productivity on site, believing that using IoT will increase safety and workflow on 56

the construction site, while PM “A1” doubted this idea, saying that IoT will not be able to save much time in the construction program because there are many factors affecting it. PM “A1” believes that experience will teach the site manager how to build a project with good productivity, adding that they will not trust a self-driving excavator on their site, believing that humans can be more aware of their surroundings than machines. PM “A1” gave an example of unexpected findings during excavation, stating that machines will not be aware and can damage archaeological finds. PM “B2” believes that technology and IoT devices will be able to increase productivity if they are used in the right way. The above data shows a contradiction between the UK based PMs about the potential of IoT in unlocking the productivity growth on construction site, PM “A1” considers the IoT technology not mature enough to be used on construction site, while PM “A2” believes that the construction industry is ready for using the IoT on site. None of the PMs was aware of smart PPE innovations such as smart working boots and smart helmets that can track worker activity on-site, give an emergency alert to prevent incidents and give accurate fatigue measurement using IoT. Nor about smart concrete-like Doka concremote that can measure concrete maturity and calibrate new age strength digitally. PM “A1” doubted the accuracy of these devices, adding that they will not use IoT devices because everything works well currently; therefore, they do not need to change or track workers. Furthermore, they already have all the information during the concrete pouring, so they do not need it digitally. All the PMs consider the resistance of people to be the most prominent barrier to the application of IoT and AI in the construction industry, believing that it is not easy to convince workers of being monitored on a timely basis. PMs “A1 and A2” added that security leaks, training, and the cost of set-up are also considerable barriers. 57

None of the PMs gave a response when they were asked if they have any recommendation of how productivity can be improved using the IoT and AI. Summary The interview helped to extract data about the problem facing the construction site productivity and the project managers point of view about using the IoT and AI in the construction industry. The result of the interview revealed that there is lack of knowledge among the UK project managers about the new IoT construction devices. Also, none of the interviewees uses any intelligent IoT devices. The interview result shows that the PMs based in Lebanon were more interested then the UK based PMs in implementing the IoT and AI technology in their construction site to improve productivity.

5 CHAPTER FIVE: DISCUSSION 5.1 INTRODUCTION The research has answers to the objectives and questions that were set at the beginning of the study. This section will discuss the interview results with the literature review to bring out succinctly our key research findings in relation to the research objectives. The aim of the research is how the Internet of Things and Artificial Intelligence can be utilised on construction sites to improve productivity. The five objectives of the research are used to highlight the key findings.

5.2 DISCUSSION AND REVIEW OF KEY FINDINGS Objective 1- The challenges that are facing productivity in the UK construction industry The literature review revealed that there are many challenges facing productivity in the UK construction industry, which is something the interviewees confirmed. Reviewing the research Sir Robert McAlpine did in 2016, that shows how young people are not interested in constructions careers; confirmed the project managers response during the interview. They admit that labour shortage is one of the major challenges the UK construction industry faces. Furthermore, lack of communication, inadequate training and client risk are some of the challenges that the interviewee highlighted on too, which is confirmed as the vicious cycle in the literature review. A lack of communication leads to delays in receiving the information and data, which in turn leads to delaying the whole project. Client risk is when the client asks for sudden changes or does not understand the project perfectly. Objective 2- How the application of IoT and AI can unlock productivity growth and improve the management on construction sites

The interviews show that the IoT technology relating to construction projects seeks the attention of most project managers in order to improve productivity on the construction sites. Nevertheless, the research found out one of the three UK project managers interviewed doubts that the IoT can save time and increase productivity on the construction site. This is the opposite of what the literature review shows. The literature review reveals that the IoT and AI will unlock the productivity growth in many ways, especially by using site monitoring, smart PPE and real-time data. Monitoring workers on site and the use of smart PPE that can detect hazards is the key to preventing delays, accidents and problems like the one that happened during the construction of Tottenham stadium. Reports stated that workers were taking drugs on site that made them lose focus and not do their task fully. This was one of the reasons for the project delay. The source, who was not named in the article, said that people were, â&#x20AC;&#x153;off their headsâ&#x20AC;? on the construction site. Workers were drinking alcohol in the morning before going on-site and taking drugs in the toilets (Martin Fricker, 2018), and the same happened during the construction of Wembley stadium (Richard White, 2006). As was mentioned in the literature review, smart PPE using IoT sensors can prevent this from happening on construction sites by monitoring workerâ&#x20AC;&#x2122;s heart rates and detecting any abnormality during their work on-site and sending the data to the site manager. Furthermore, none of the interviewees uses augmented reality on-site. The importance of augmented reality in streamlining tasks was highlighted in the literature review. It can also prevent mistakes by showing the workers how the project will look and how a specific task should be by virtually using smart glasses, which can increase the productivity on site. Nevertheless, the literature review revealed that using the autonomous vehicle will bring better efficiency and increase productivity on the site, and also, it can protect the drivers from

serious accidents or site errors due to tiredness. This technology had opposition from an interviewee, considering the self-driving machinery are untrustworthy. Objective 3- The sophistication, use, advantages, and disadvantages of IoT and AI in the construction industry As the literature review revealed, using IoT and AI in the construction industry and built environment has many advantages, such as increasing security using facial recognition cameras, site monitoring, labours and machinery tracking. Plus, increasing safety using Heart monitoring devices, smart PPE that can detect tiredness and danger and also developing new and more uncomplicated building technique to decrease the risk of errors using Augmentation reality. Nevertheless, as much as the advanced IoT devices will bring advantages for the construction industry, there will be disadvantages and side effects of this digital technology. The project managers highlighted some disadvantages of using the IoT, and the literature review confirmed it. These disadvantages can be dangerous such as cyber-attack on the IoT devices, that may lead to steal data, prevent the site manager from accessing the data and lead to security leak. Moreover, if the site is using an autonomous vehicle that is being controlled remotely, then the hacker will be able to disturb the signal and take control over the vehicle, which means a risk of great danger. Furthermore, as the literature review acknowledges, IoT and AI need a continuous connection, which means that if the IoT devices lost connectivity, it will create a blind job site and let the site managers unaware of what is happening on the construction site. This connection risk may lead to delays, accidents and critical errors.

Objective 4- Barriers to the application of IoT and AI in the construction industry The result of the research interview reveals that all project managers believe that the most significant barrier to the application of IoT and AI in the construction industry is the resistance of people. Some people disapprove of changing, and others are concern about data privacy. Brian Buntz (2016) supported this, as the literature review revealed, by showing that the highest percentage of participants who are not embracing the IoT are concern about data privacy. Furthermore, the literature review revealed that a high percentage of people are not comfortable about the potential leak of security in the IoT devices, as well as the high cost of the implementation. The interview result declares the same by considering the security and cost as a considerable barrier to the application of IoT and AI in the construction industry. The research exposes some doubts from a project manager about the accuracy of IoT devices, considering that it is a marketing game, and these devices will not save time on the construction site. Also, the literature review revealed, that people concern about IoT devices and the fear that these devices will not deliver benefits as promised, is considered another barrier for the application of the IoT and AI in the industry. Artificial intelligence and advanced IoT devices are an innovation, which means they are unmatured, still under testing and continuous development. In other words, there is a lack of knowledge about the risk of this technology, and there is a lack of people who are trained to use it. The research result revealed that training people to use this technology is considered a barrier, along with the lack of knowledge about the risks of using it and the available solutions for these risks.

Objective 5- Recommendations for how we can combine IoT with AI to achieve productivity growth The literature review revealed that Artificial Intelligence could give better value for the Internet of Things, and it can make the IoT devices smarter and improve the accuracy rate. AI makes IoT applications realise their full potential. Artificial intelligence enables the ability of the machine to learn, which will bring more detailed data at a faster rate, that will lead to better operational efficiency. Furthermore, the IoT devices will be able to monitor machines and report any error, such as equipment failure using real-time data sharing. By adding AI to the process, the machines will be able to perform predictive analysis. In other words, the machine will detect the failure and give the right instruction to mitigate it before it even happens.

6 CHAPTER SIX: CONCLUSIONS AND RECOMMENDATIONS 6.1 SCOPE OF CHAPTER The literature review findings and the interview results were able to respond to the research aim and the objectives set at the beginning of the research. This chapter provides a summary of the findings with there conclusions, the overall conclusion, personal recommendation, and the limitation of this research.

6.2 RESEARCH OBJECTIVES: SUMMARY OF FINDINGS AND CONCLUSIONS

Research objective 1: Investigate the challenges that are facing productivity in the UK construction industry â&#x17E;˘ Summary of findings From the research results and findings, the challenges that are facing productivity in the UK construction industry are as follows: I.

Labour shortage and lack of skilled labours due to the young student ignorance and dislike for construction careers.

II.

Lack of communication due to many layers between the client and contractor

III.

Inadequate training

IV.

Lack of resources â&#x17E;˘ Conclusion

Several challenges are affecting the UK construction site productivity; however, the ones highlighted are obtained from the result of the research. These factors are found to be challenging in order to unlock the productivity growth in the construction site, and especially

in the UK. This concludes that the objective was well addressed through literature review and supported by the interview results.

Research objective 2: Identify how the application of IoT and AI can unlock productivity growth and improve the management on construction sites

â&#x17E;˘ Summary of findings As the research revealed, the IoT and AI can unlock and improve management and productivity on the UK construction site as follow: I. II.

Monitoring the site using IoT devices and real-time data sharing Using smart PPE for better health and safety on site

III.

Using the Augmentation Reality technology, for fewer errors during the work.

IV.

Using autonomous machinery to decrease driver error during tiredness

V. VI.

Using concrete sensors which can save time by giving early concrete strength Using intelligent prefab to track individual prefab through the supply chain and decrease the delay risk during construction

VII.

Using Building Information Modelling during the design period to decrease the errors during the construction period â&#x17E;˘ Conclusion

Advanced IoT and AI devices are the keys to improve productivity on the UK construction site in the 21st century due to the technological race to solve all the traditional problems digitally. The research objective two was fully extracted from the literature review.

Objective 3: Classify The sophistication, use, advantages, and disadvantages of IoT and AI in the construction industry

â&#x17E;˘ Summary of findings Based on the research finding, the summary of the advantages and disadvantages of IoT and AI in the construction industry are as follow: I.

II.

Advantages of IoT and AI i.

Decrease the time needed to do a specific task

ii.

Reduce Machinery and labours errors

iii.

Increase safety

iv.

Boost the security level

Give higher accuracy and better job quality

vi.

Improve the on-site performance

Disadvantages of IoT and AI i.

Security leak

ii.

Higher risk of cyber attack

iii.

Connectivity issues

iv.

Privacy issue

Risk of Unemployment

â&#x17E;˘ Conclusion The research shows that as much as there are advantages for the application of IoT in the construction industry, there are disadvantages that will lead to serious problems. In conclusion, there is a risk in order to benefits from this advanced technology. 66

Objective 4: Identify the barriers for the application of IoT and AI in the construction industry â&#x17E;˘ Summary of findings The barriers to the application of IoT and AI in the construction industry are revealed using the literature review and interview results. The summary of the barriers is as follow: I. II. III.

The resistance of people to change and adapt this technology Potential leak of security Cost of implementing this technology and not knowing the difference between cost and value of benefits

IV. V. VI. VII.

Training people to use it People suspicion about the IoT and AI maturity Data privacy concern Lack of standards

â&#x17E;˘ Conclusion The research revealed the barriers that are affecting the IoT and AI technology from being adopted in the UK construction industry and showed that convincing people and their fear to use this technology is the biggest barrier.

Objective 5: Make recommendations for how we can combine IoT with AI to achieve productivity growth

â&#x17E;˘ Summary of findings The research revealed recommendation on IoT could be combined with AI to achieve better productivity growth. These recommendations are summarised as follow:

Implementing the AI with the IoT monitoring devices will make these devices smarter and will create more productive Jobsite

II.

Autonomous vehicles are controlled remotely using the IoT. Implementing AI in these machines will make them smarter, self-thinker and able to do the work without human interference.

III.

AI with the use of IoT can manage a whole project while giving the possible risks and possible mitigation methods before they happen

IV.

AI can be used in the IoT machinery sensors, providing better information about the machinery performance, and detect errors and malfunctions before they happen.

â&#x17E;˘ Conclusion IoT and AI are two different things; each one has its own power and benefits, merging them will create a new future for how everything works and increase the ability of the IoT with further possibilities.

6.3 OVERALL CONCLUSION The outcome of the research provided adequate information to answer the research question on how the Internet of Things and Artificial Intelligence can be used to unlock the productivity growth in the UK construction site. The research revealed that there are many possibilities for the IoT and AI to be used in the UK construction industry, but there are also few risks for using it, that the construction sector should be prepared for it. Also, the interview showed that in a different country, people are more excited about implementing IoT and AI in the construction industry in order to improve productivity. The good thing is that this innovation is still new, which mean IoT risks mitigations are the priority for many developing companies. Also, innovation is being developed to decrease the risk of using these IoT devices, such as WiFi 6, 5G connectivity and stronger anti-hacking systems. This leaves convincing people to adopt this technology is the major barrier in order to start increasing the UK construction productivity. Furthermore, the literature review revealed the use of IoT in the other sectors, especially in the medical sector, and showed how the IoT with the use of AI is helping doctors to detect early health problems and how to cure it and monitor patients health remotely. This shows that the construction sector is far behind the medical and other sectors in implementing the Internet of Things.

6.4 RECOMMENDATIONS The results of the research between the literature review and the interviews have shown a level of consistency in addressing the barriers to adopt the Internet of Things and Artificial Intelligence in the construction industry and the ideas of how this technology can be utilised to improve the productivity on the UK construction site. This study has shown how important is the use of IoT and AI in the construction industry and the problems this technology can reduce and solve. However, to add more ideas on how productivity can be increased in the UK construction site using the IoT and AI, and how to decrease people resistance, personal recommendations have been added.

Construction and Engineering university students should be taught about the benefits of IoT and AI and how this technology can change the construction site to a better workplace with better productivity.

II.

Provide trial availability for the IoT and AI devices for construction companies to prove the benefits of it and how it can increase productivity

III.

The IoT and AI developer companies should provide free or low-cost training on how these devices can be used on construction site

IV.

The young students should be taught about how easy and fun the work can be on construction sites using the new IoT and AI devices, in order to make the construction sector an attractive and exciting career.

Similar research to be made on a broader version and to a different group to validate the findings of this research

6.5 LIMITATION OF THE RESEARCH The research has reached its aim. It however encountered some limitations during the study. These limitations are written below. I.

IoT and AI devices are very new, and the companies that are using them are minimal, which make extracting the secondary data from previous case studies challenging and limited.

II.

Few project manager replied to the email asking for an interview, which made the primary data limited to three project managers in the UK and two in Lebanon.

III.

Most of the companies that use this technology now are located in China, which was a big barrier to get more primary data on how the IoT and AI can increase productivity on the construction site.

IV.

Investigating the challenges facing the construction industry as a whole will take a significant amount of time and research, due to how extensive the industry is, the research results priorities on how to increase productivity on the construction sites.

IoT and AI devices technology are still very new and under development, and very few companies are using this technology on construction sites, which made the information on the risk of using it on construction sites limited and small amount of ideas on how to solve it.

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8 APPENDICES

8.1 APPENDIX 1: ETHICS CHECKLIST Section A Project details - to be completed by the project student

1. Name of student/s: Jad Zawil 2. Name of supervisor: Dr Hannah Wood 3. Title of project (no more than 20 words): Improving construction site productivity using the Internet of Things 4. Outline of the research (1-2 sentences): 5. Timescale and date of completion: 29/08/2019 6. Location of research: United Kingdom 7. Course module code for which research is undertaken: MSc Project Management for construction 8. Email address: jadzawil@gmail.com 9. Contact address: 10. Telephone number: 07943918077

Section B Ethics Checklist questions

Please tick the appropriate box 1. Is this research likely to have significant negative impacts on the environment? (For example, the release of dangerous substances or damaging intrusions into protected habitats.) 2. Does the study involve participants who might be considered vulnerable due to age or to a

Yes

No x x

social, psychological or medical condition? (Examples include children, people with learning disabilities or mental health problems, but participants who may be considered vulnerable are not confined to these groups.) 3. Does the study require the co-operation of an individual to gain access to the participants? (e.g. a teacher at a school or a manager of sheltered housing)

4. Will the participants be asked to discuss what might be perceived as sensitive topics? (e.g. sexual behaviour, drug use, religious belief, detailed financial matters)

5. Will individual participants be involved in repetitive or prolonged testing?

6. Could participants experience psychological stress, anxiety or other negative consequences (beyond what would be expected to be encountered in normal life)?

7. Will any participants be likely to undergo vigorous physical activity, pain, or exposure to dangerous situations, environments or materials as part of the research?

8. Will photographic or video recordings of research participants be collected as part of the research?

9. Will any participants receive financial reimbursement for their time? (excluding reasonable expenses to cover travel and other costs)

10. Will members of the public be indirectly involved in the research without their knowledge at the time? (e.g. covert observation of people in non-public places, the use of methods that will affect privacy)

11. Does this research include secondary data that may carry personal or sensitive organisational information? (Secondary data refers to any data you plan to use that you did not collect yourself. Examples of sensitive secondary data include datasets held by organisations, patient records, confidential minutes of meetings, personal diary entries. These are only examples and not an exhaustive list).

12. Are there any other ethical concerns associated with the research that are not covered in the questions above?

All Masters level projects or dissertations in the School of Environment and Technology must adhere to the following procedures on data storage and confidentiality : Once a mark for the project or dissertation has been published, all data must be removed from personal computers, and original questionnaires and consent forms should be destroyed unless the research is likely to be published or data re-used. Please sign below to confirm that you have completed the Ethics Checklist and will adhere to these procedures on data storage and confidentiality. Then give this form to your supervisor to complete their checklist. Signed (Student): Date:

8.2 APPENDIX 2: INTERVIEW FORM 1)

Briefly describe what is your project about and when is the deadline?

How would you define productivity?

What strategies are you using to increase productivity on your project site?

What are the challenges you are facing in your efforts to improve productivity on your site?

Have you ever used augmentation reality on any of your project sites?

Are you aware of the internet of things (IoT) and artificial intelligence (AI) devices?

Are you using any form of the IoT and AI in your project?

If not, why?

Are you aware that using IoT on-site can increase productivity?

10) Are you aware of innovations such as ‘smart working boots’ that can track worker activity on-site and give an emergency alert as well as prevent struck-by incidents? Or ‘smart caps’ which can be inserted in the safety helmet to determine alertness and eliminates microsleeps with accurate fatigue measurement which can prevent incidents on-site that originate from fatigue or tiredness? Or about intelligent concrete-like Doka concremote that uses GSM enabled digital sensors to measure the concrete maturity (temperature x time) and calibrates early age strength? 11) What do you think are the potentiality of using IoT and AI to improve productivity on construction sites? 12) What would you consider as barriers for the application of IoT and AI in the construction industry? 13) Do you have any recommendations on how we can improve productivity using IoT and AI on constructions?

8.3 APPENDIX 2: TABLE OF INTERVIEW

Questions

Project Manager A1

Project Manager A2

Briefly describe What is your project about and when is the deadline? How would you define productivity?

Student accommodation and affordable housing Comparing Work Quality received with a specific time agreed on with the client Stick with the Contract program, meetings with contractors and clients, create a pleasant working environment

A new sports block for college

What strategy are you using to increase productivity on your project site?

Project Manager A3 Different projects

Project Manager B1

Project Manager B2

Different projects

Resource going/amount of work produce

Resources/ Resources/amount of amount of work work

Work produced depend on the resources

Full cast program, labour resource program, focus on health and safety, give the worker all the information they need. Set a goal and try to achieve it.

Improving communication with the client and increase the flow of information

Make meetings with workers, motivate the labours, use the work breakdown structure to organize the work.

Selecting best people available, best project managers who have past experience in a similar project, labours who work on fast track and under pressure, allocate good resources. Tracking labour works on a weekly basis and checks the result to see if there is a lack in productivity.

What are the challenges you are facing in your efforts to improve productivity on your site?

Logistic challenges, lack of communication, labour shortage, accident due to mistake and tiredness, which lead to delays.

The flow of information, breakdown in communication, information gets delayed to be received, resource level.

Client risk, the flow of information in communication

Have you ever used augmentation reality on any of your project sites? Are you aware of the internet of things and artificial intelligence devices can save time and prevent delays and incident? Are you using any form of the IoT on-site and AI in your project?

Doing meetings and work breakdown structure. Internal challenges: resource risk, technological risk, lack of training. External challenges: client risk if he asked for a design change or want the project earlier which lead to affect the project life cycle. Yes

I’m aware of IoT but I don’t believe it will save time

Yes

No, but we are planning to start using it in the Enterprise resource planning system

If not, why?

We don’t need it; we have everything in front of us. We can track worker using fingerprint gate

The lack of knowledge, cost, not knowing the difference between cost and value of benefits.

We have Small projects; client won’t pay for that, cost

Material delivery delays, lack of communication between worker on site.

Lack of knowledge and lack of people trained to use it.

Are you aware that using IoT onsite can increase productivity?

Are you aware of innovations such as ‘smart working boots’ that can track worker activity onsite and give an emergency alert as well as prevent struck-by incidents? Or ‘smart caps’ which can be inserted in the safety helmet to determine alertness and eliminates microsleeps with accurate fatigue measurement which can prevent incidents onsite that originate from fatigue or tiredness? Or about intelligent concrete-like Doka concremote that uses GSM enabled digital sensors to measure the concrete maturity (temperature x time) and calibrates early age strength? 11 What do you think are the potentiality of using IoT and AI to improve productivity on construction sites?

Maybe, IoT can’t save time in the program because there is a lot of factors to it, technology won’t make construction faster. IoT is not mature enough.

Yes

Only if it’s been using in the right way

No, and I don’t think we will use that. We don’t need to change. We have all the information when we pour concrete; we don’t need it digitally

You can’t use it in any project. The experience will teach the site managers how to build a

Massive potential, IoT can help put everything in the right place

Construction in the UK is slow to take up, but when the companies and

Big potential. increase safety, security, workflow.

Good potentials, make things easier.

12 what would you consider as barriers of the application of IoT and AI in the construction industry?

project with good productivity. Maybe in the future, when the IoT is mature enough we may use it In complex project. Cost, training worker, the resistance of people, convince people.

client know the benefits of it, then thereâ&#x20AC;&#x2122;s good potential.

Security leak, cost, the resistance of people, not easy for people to accept that they are being monitored.

The resistance of people and cost

The resistance of people, convince people.

Cost, training worker, the resistance of people, convince people.

8.4 APPENDIX 4: RISK ASSESSMENT FORM 1School:

Environment and Technology

Date of assessment:

Activity / area:

Next review date:

Assessed by:

Jad Zawil

Checked by:

No.

What are the hazards?

Public transport accident, traveling alone

Persons at risk & how they may be harmed Just me, injuries

Risk Rating What controls do you already have in place?

Tell a family member where am I and where Iâ&#x20AC;&#x2122;m going before taking the public transport

Severity

Likelihood

Risk

Additional controls needed to reduce the risk if required

Carry a smart phone with gps on it with me the whole time

Action: date & responsible person

8.5 APPENDIX 5: PARTICIPANT INFORMATION SHEET

Participation Information Sheet Template My name is Jad zawil, I’m doing my master degree in project management for construction at University of Brighton, and I’m doing a research about how we can improve productivity in the construction site ( you can find all details needed written in this document). Title of Study Using technology to improve productivity in the construction site Introduction and what is the purpose of the study/project? The UK Construction industry is plagued with some severe challenges in low productivity and profitability. Delivering the project on budget and to schedule is a challenge that has always plagued the construction industry. Advancement of technology in the use of the Internet of things (IOT) and artificial intelligence (AI) enable its application in a variety of ways in numerous sectors of the economy. The construction industry may be lacking behind in adopting these technologies mainly on the operations conducted on project sites. This research aims to make recommendations as to how IoT and AI can be utilised on construction sites to improve productivity. Invitation paragraph I would like to invite you to take part in my research study. Participation in this research will be using an interview. This should take about 15 to 20 minutes. if there is anything that is not clear, you can ask to clarify the purpose of this research. You will be given time to think about whether you wish to take part before making a decision. Why have I been invited to participate? This research is related to project management and construction industries. Your experience in this field will be helpful for my research. Do I have to take part? It’s voluntary and you can withdraw anytime you want. What will happen to me if I take part? An interview will be taken place after agreeing on time and place that suit you. The interview will be recorded and there will be some survey question I would like to ask you during the interview. The interview will be between 30 to 60 minutes. Will I be paid for taking part? No, it’s voluntary.

What are the potential disadvantages or risks of taking part? Thereâ&#x20AC;&#x2122;s no risk of taking part in this research, but if you felt uncomfortable, the interview will stop. What are the potential benefits of taking part? It will help to find the reasons for low productivity on the construction sites and find solution to increase the productivity using technology. Will my taking part in the study/project be kept confidential? Yes, all data will be password encrypted, and it will be removed once the research is submitted and award is given. What will happen if I donâ&#x20AC;&#x2122;t want to carry on with the study? You can withdraw anytime you want, and data will be removed if you asked to. But after 1st of August, data will no longer be possible to be removed, since removing Data during the writing-up stage will be very difficult. What will happen to the results of the project? The research is a master degree dissertation and it will be submitted to university of Brighton Who is organising and funding the research? The university of Brighton What if I have a question or concern? Any queries or concerns will be addressed, and refer to the contact details below. Contact details Kassim Gidado, Hannah Wood K.I.Gidado@brighton.ac.uk hw35@brighton.ac.uk Who has reviewed the study? the study has been reviewed and given a favourable ethical opinion by the relevant Research Ethics Committee or Panel

Improving construction site productivity using the Internet Of Things

Articles inside

2.12.5 Using Intelligent prefab

2.12.4 Using Building Information Modelling (BIM