REMOTE-CONTROLLED TECHNOLOGY ASSESSMENT FOR SAFER CONSTRUCTION by PITT

UNIVERSITY OF PITTSBURGH | SWANSON SCHOOL OF ENGINEERING | CIVIL & ENVIRONMENTAL

S U M M A RY R E P O R T

Remote-Controlled Technology Assessment for Safer Pavement Construction and QA/QC I R I S E - 2 2 - P 2 1- 01- 01 • J U LY 2 0 2 2 Photo courtesy of Maynard Factor from Kratos Defense

Photo courtesy of FHWA and ARA

Technical Report Documentation Page 1. Report No.

2. Government Accession No.

3. Recipient’s Catalog No.

FHWA-PA-2022-008-IRISE WO 05 4. Title and Subtitle

5. Report Date July 2022 6. Performing Organization Code

Remote-Controlled Technology Assessment for Safer Pavement Construction and QA/QC 7. Author(s)

8. Performing Organization Report No. IRISE-22-P21-01-01

Lev Khazanovich, Lucio Salles and Katelyn Kosar 9. Performing Organization Name and Address Department of Civil and Environmental Engineering University of Pittsburgh Benedum Hall 3700 O'Hara Street | Pittsburgh, PA 15261

10. Work Unit No. (TRAIS) 11. Contract or Grant No.

12. Sponsoring Agency Name and Address

13. Type of Report and Period Covered

The Pennsylvania Department of Transportation Bureau of Planning and Research Commonwealth Keystone Building 400 North Street, 6th Floor Harrisburg, PA 17120-0064

April 2021 – July 2022 14. Sponsoring Agency Code

15. Supplementary Notes PI contact info: 742 Benedum Hall, 3700 O’Hara Street, Pittsburgh, PA 15261 – lev.k@pitt.edu 16. Abstract Pavement construction, inspection and maintenance are activities that often require workers near heavy equipment, traffic, and dangerous materials. This proximity to potential hazards along with the characteristics of highway and street work zones - transient and restricted areas - increases the possibility of accidents and near-misses. Recent developments in remote-controlled technology can provide workers and inspectors the ability to conduct activities from a safer distance. This project aimed at scanning and evaluating several promising remote-controlled technologies that could be used to improve safety in highway and streets work zones. The technology scanning disclosed over twenty technologies in several levels of development that met this goal. Three technologies were selected for a more detailed review: the Remote-Controlled GPR for Asphalt Density, the Automated Real-Time Thermal Profiling for Asphalt Paving, and the Autonomous Impact Protection Vehicle. Each technology was evaluated not only based on safety features but also on productivity, data processing, and, specially, requirements for implementation. Workshops with vendors and leading experts were promoted for each technology. Field demonstrations were conducted for the Density Profiling System for determining in-situ asphalt density. Finally, several recommendations for implementation of the selected technologies were provided. 17. Key Words safety; work zone; remote-controlled; tech scan; 19. Security Classif. (of this report)

20. Security Classif. (of this page)

Unclassified

Form DOT F 1700.7

(8-72)

18. Distribution Statement No restrictions. This document is available from the National Technical Information Service, Springfield, VA 22161 21. No. of Pages

22. Price

99 Reproduction of completed page authorized

IRISE

Acknowledgements

The Impactful Resilient Infrastructure Science & Engineering consortium was established in the Department of Civil and Environmental Engineering in the Swanson School of Engineering at the University of Pittsburgh to address the challenges associated with aging transportation infrastructure. IRISE is addressing these challenges with a comprehensive approach that includes knowledge gathering, decision making, design of materials and assets and interventions. It features a collaborative effort among the public agencies that own and operate the infrastructure, the private companies that design and build it and the academic community to develop creative solutions that can be implemented to meet the needs of its members. To learn more, visit: https://www.engineering.pitt.edu/irise/.

The authors gratefully acknowledge the financial support of all contributing members of IRISE. In addition, we are indebted to the advice and assistance provided by Neal Fanning, Matthew Connolly, and Shelley A. Scott of the Pennsylvania Department of Transportation, Clint Beck and Yathi Yatheepan of the Federal Highway Administration, Jason Molinero of Allegheny County, and Edward Skorpinski of the Pennsylvania Turnpike Commission. The authors would also like to acknowledge the speakers of the technology transfer workshops, namely, Maynard Factor of Kratos Defense, Thomas Pucci of Royal Truck & Equipment, Joe Reiter of Applied Research Associates, and Kyle Hoegh of the Minnesota Department of Transportation, and Ken Corcoran of GSSI.

Disclaimer The views and conclusions contained in this document are those of the authors and should not be interpreted as representing official policies, either expressed or implied, of the Pennsylvania Department of Transportation, the Pennsylvania Turnpike Commission, Allegheny County, Golden Triangle Construction, Michael Baker International, the Constructors Association of Western Pennsylvania or CDR Maguire.

Table of Contents 1. Introduction ................................................................................................................................. 1 2. Technology Scanning.................................................................................................................. 2 2.1. IRISE Survey ..................................................................................................................................... 3 2.2. Remote Controlled Tech-Scan for Safer Pavement Work ................................................................. 3 2.2.1 Construction................................................................................................................................................. 7 2.2.1.1 Automated Machine Guidance (AMG) and Control (AMC) ............................................................... 7 2.2.1.2 Fully Automated Construction Equipment .......................................................................................... 8 2.2.1.3 Robots and unmanned aerial systems (UAS) for Project Layout and Stakeout .................................. 9 2.2.1.4 Demolition Robots ............................................................................................................................... 9 2.2.1.5 UAS for Land Surveying ................................................................................................................... 10 2.2.1.6 UAS for Earthwork Volumetrics ....................................................................................................... 10 2.2.2 Pavement Marking ..................................................................................................................................... 11 2.2.2.1 Automated Marker Placement ........................................................................................................... 11 2.2.3 Inspection ................................................................................................................................................... 11 2.2.3.1 Remote-Controlled Ground Penetrating Radar (GPR) for Asphalt Density ...................................... 11 2.2.3.2 Integrated GPR and Infrared (IR) Imaging in Non-destructive Testing (NDT) Vehicle ................... 12 2.2.3.3 E-Construction Inspection ................................................................................................................. 12 2.2.3.4 Remote-Controlled Inspection of Conduits ....................................................................................... 13 2.2.3.5 Remote Data Collection ..................................................................................................................... 14 2.2.3.6 Robot for Asphalt Silo Inspection...................................................................................................... 14 2.2.3.7 Automated Real-Time Thermal Profiling for Asphalt Paving........................................................... 15 2.2.3.8 Robots for Inspection ......................................................................................................................... 16 2.2.3.9 Wireless Maturity Sensors ................................................................................................................. 17 2.2.3.10 UAS for Photography and Videography .......................................................................................... 17

2.2.4 Maintenance ............................................................................................................................................... 18 2.2.4.1 UAS for Pavement Condition and Distress Analysis ........................................................................ 18 2.2.4.2 Remote-Controlled Mowers............................................................................................................... 18 2.2.4.3 Automated Crack/Joint Sealing ......................................................................................................... 19 2.2.4.4 Remote-Controlled Tree Management .............................................................................................. 20 2.2.4.5 Remote-Controlled Roadside Litter and Debris Removal ................................................................. 21 2.2.4.6 Robot for Culvert Cleaning................................................................................................................ 22 2.2.5 Work Zone Safety ...................................................................................................................................... 23 2.2.5.1 Autonomous Impact Protection Vehicle ............................................................................................ 23

3 Selected Technologies ............................................................................................................... 24 3.1 Thermal Profiling for Asphalt Paving............................................................................................... 25 3.1.1 Proposed Technology 1: Automated Real-Time Thermal Profiling for Asphalt Paving .......................... 28 3.1.1.1 Advantages and Disadvantages.......................................................................................................... 32 3.1.1.2 Past and Present Experience .............................................................................................................. 34 3.1.1.3 What is needed for PA implementation? ........................................................................................... 36

3.2 Ground Penetrating Radar (GPR) for Asphalt Density..................................................................... 37 3.2.1 Proposed Technology 2: Remote-controlled GPR .................................................................................... 40 3.2.1.1 Advantages and Disadvantages.......................................................................................................... 42 3.2.1.2 Past and Present Experience .............................................................................................................. 44 3.2.1.3 What is needed for PA implementation? ........................................................................................... 52

3.3 Impact Protection Vehicle for Work Zones ...................................................................................... 54 3.3.1 Proposed Technology 3: Autonomous Impact Protection Vehicle............................................................ 57 3.3.1.1 Advantages and Disadvantages.......................................................................................................... 58 3.3.1.2 Past and Present Experience .............................................................................................................. 63 3.2.1.3 What is needed for PA implementation? ........................................................................................... 66

4. Summary and Recommendations ............................................................................................. 69

4.1. Automated Real-Time Thermal Profiling for Asphalt Paving......................................................... 69 4.2. Remote Controlled Density Profiling System.................................................................................. 70 4.3 Autonomous Impact Protection Vehicle ........................................................................................... 71

References ..................................................................................................................................... 73 Appendix A: IRISE survey ........................................................................................................... 80 Appendix B: Technology Transfer Workshops ............................................................................ 82

List of Figures Figure 1: Pavement construction flow using AMG equipment [11]............................................... 8 Figure 2: Remote controlled earthwork equipment [13]................................................................. 9 Figure 3: Robot and drone for road layout and stakeout [14] ......................................................... 9 Figure 4: Example of bridge deck demolition using a remote-controlled robot [15] ................... 10 Figure 5: Marker placement with (a) manual method and (b) automated system [19]................. 11 Figure 6: Conduit remote inspection using (a) crawler robot (b) UAS [22] ................................. 13 Figure 7: Remote data collection system [23] .............................................................................. 14 Figure 8: Robot for silo inspection [24]........................................................................................ 15 Figure 9: Infrared sensors attached to paver for real-time thermal data acquisition [26] and the latest version of IR temperature scanners [27] ............................................................................. 16 Figure 10: Robot for bridge deck inspection [29]......................................................................... 17 Figure 11: Remote-controlled mower in a slope [34] ................................................................... 19 Figure 12: Longitudinal sealing machine [35] .............................................................................. 20 Figure 13: Telescoping grapple saw for roadside tree removal [36] ............................................ 20 Figure 14: Automated Roadway Debris Vacuum [37] ................................................................. 22 Figure 15: Litter removal attachment operation [38] .................................................................... 22 Figure 16: Trash-harvesters prototype [39] .................................................................................. 22 Figure 17: Two remote controlled culvert cleaning machines [41, 42] ........................................ 23 Figure 18: Autonomous impact protection vehicle [44] ............................................................... 24 Figure 19: Temperature segregation identified with thermal imaging [47] .................................. 26 Figure 20: Distress due to temperature segregation causing inadequate compaction [50]. .......... 27

Figure 21: Infrared sensors attached to paver for real-time thermal data acquisition [52,53] ...... 29 Figure 22: On-board computer output for real time feedback [53] .............................................. 30 Figure 23: Examples of Pave Project ManagerTM detailed reports with temperature profiles and paver speed or time diagram [53] ................................................................................................. 31 Figure 24: Cleaned temperature profile [52] ................................................................................ 33 Figure 25: PDP instrument background principle of operation [73]. ........................................... 39 Figure 26: DPS (courtesy of MnDOT) ......................................................................................... 40 Figure 27: A prototype of MnDOT remotely operated rolling asphalt density meter .................. 41 Figure 28: Core Measured Air Void vs. Ground Penetrating Radar Dielectrics for Section 1 [63] ....................................................................................................................................................... 45 Figure 29: Section 3 Correlation Model [63]................................................................................ 45 Figure 30: Real-time data visualization and comparison with cores [63] .................................... 46 Figure 31: Cherryfield, Maine calibration model [63].................................................................. 47 Figure 32: Joint survey [63] .......................................................................................................... 48 Figure 33: Dielectric Maps from Joint Surveys of I-95 near Pittsfield, Maine [63] ..................... 50 Figure 34: Eyota, Minnesota calibration model for all cores [63] ................................................ 51 Figure 35: Impact testing of TMA on a tractor [89] ..................................................................... 55 Figure 36: Accident involving IPV of the Virginia DOT [92] ..................................................... 56 Figure 37: AIPV system overview [95] ........................................................................................ 57 Figure 38: AIPV system layout [97] ............................................................................................. 58 Figure 39: Frequency distributions histograms of the cross-track error in following distance and accuracy tests: (a) following accuracy, (b)lane changing, (c) roundabout operation, (e) minimum turn radius, (f) U-turn [86] ............................................................................................................ 62

Figure 40: Density Profiling System (DPS) in a) robotic platform, and (b) conventional man operated cart. ................................................................................................................................. 71

List of Tables Table 1: Identified remote-controlled technology for pavement construction, marking and inspection ........................................................................................................................................ 5 Table 2: Identified remote-controlled technology for pavement maintenance and work zone safety ......................................................................................................................................................... 6 Table 3: Specification recommendations for LaDOTD [48]. ....................................................... 36 Table 4: Mean and Median Air Void Contents for Mid-lanes and Joints [63] ............................. 51 Table 5: Summary of AIPV testing results [96] ........................................................................... 61

1. Introduction Pavement construction, inspection, and maintenance are usually conducted in restricted areas (width of road or single lane) making workers and inspectors perform their activities in close proximity to heavy equipment, construction vehicles, and dangerous materials. Pavement activities are also typically conducted near passing private traffic. In addition, paving work-zones are transient, moving along as construction progresses in contrast to building construction which is usually stationary from start to finish. This makes it hard to provide safety design and training for highway construction based on the characteristics of the area. As a result of these particular characteristics, pavement related work can be a risky activity. Struck-by accidents are the leading cause of fatalities and injuries in the highway industry, for both high-traffic highway and local street construction and maintenance [1]. Construction safety research has been focused mostly on the major cause of struck-by accidents in roadway construction and maintenance which is passing private traffic entering the work-zone [2-5]. However, accidents also occur inside work zones during normal activities due to worker and inspector interactions with heavy equipment, falls, workers struck-by material, and many others [2, 6, 7]. Moving personnel away from these dangerous scenarios so that they can perform their work activities from a safer distance can be a way to decrease the number of accidents in the paving industry. Remote-controlled and automated technologies and processes have been used to allow workers to conduct activities from a safer distance in several industries over the last decades. The use of these technologies has made working in mining, demolition, underwater operations, and contaminated areas substantially safer than in the past. Recent developments in computer-guided machinery and intelligent systems have advanced a new set of robots, drones and other equipment that can be incorporate into engineering construction. Such developments have been used in bridge and tunnel construction and health monitoring for example. In addition to safety benefits, these technologies can also increase productivity and quality of construction while being less prone to human errors. This research project has the objective of evaluating several remote-controlled technologies and processes that are currently being developed, tested, or used in the paving 1

industry. For that, we scanned several technologies in different stages of development with potential for use in pavement construction, inspection, and maintenance. The more promising of these technologies were selected for an in-depth review including an overview of the conventional technology or process, highlighting the safety concerns or other short comings. Then, the proposed remote-controlled technology that could be used to improve the current practice was explained including advantages, disadvantages, and experience from other State transportation agencies, practitioners, and research institutions. Moreover, this project aimed to describe the requirements and challenges of implementing these technologies in Pennsylvania considering the potential acceptance of technology, changes to existing specifications, required testing and training, and potential vendors and cost providing recommendations for implementation.

2. Technology Scanning Infrastructure research funding is scarce, however, there is a high expectation to provide clear benefits from research. Thus, funds must be applied appropriately to support innovative and practical technology development that will help sponsoring partners. Technology scanning is a type of research where the main focus is to investigate technologies developed or are in development by other research groups and institutions. A tech scan provides many benefits: it avoids duplicate research efforts, facilitates the transfer of knowledge noting both successes and failures, and most importantly, accelerates the use of beneficial technology without the need to spend resources in a research project to fully develop a technology from scratch [8]. Initially, we surveyed the Impactful Resilient Infrastructure Science and Engineering (IRISE) community to prioritize areas and technologies that the members deemed important or interesting for this project. Then, several promising technologies were identified and are briefly presented in this report with special consideration on the technology development status and applicability to the Pennsylvania paving industry. Vendors and developers for the technologies were also identified.

2.1. IRISE Survey A simple survey was conducted with IRISE members to help prioritize the tech scan. The survey asked which areas of pavement construction, inspection, and testing do IRISE members think would benefit the most from a remote-controlled approach with focus on work safety. In addition, we asked members if they already knew of or were interested in any existing technology. The responses to the survey can be found in Appendix A. The responses showed interest in areas like paving equipment that can provide real-time data about the work, grading operations, and pavement condition analysis. In addition, respondents are clearly interested in remote-controlled technology for bridge construction and inspection, however that is out of scope of the present project which is focused solely on pavement related activities. The state-of-the-art remote-controlled technology for bridge inspection is well developed and has been somewhat rapidly adopted by the construction industry. It is recommended that a tech scan for emerging remote-controlled technology for bridge construction and inspection be considered. For the technologies that members were already interested, several of the proposed technologies, while indeed very interesting, are not remote-controlled or do not offer any safety improvements, and therefore are out scope of the project. Members showed interested in camera systems that could inspect truck bed cleanliness for asphalt transportation and concrete truck fins in truck mixers. We were not able to identify any feasible technology for these activities. Members were also interested in automated or remote-controlled equipment for paving and compaction. These technologies were scanned in this task including partially and fully automated equipment. 2.2. Remote Controlled Tech-Scan for Safer Pavement Work The remote technology scanning was performed by researching recent technology developments from research institutions in the US and abroad. We particularly looked at what remote-controlled process and technologies other state department of transportation (DOTs) are using. We also looked at relevant news and companies with remote-controlled products that could be used in the paving industry. Initially, the tech scan was focused on pavement construction and inspection only, but as many interesting remote technologies for maintenance 3

were identified, we decided – with approval from the Technical Panel – to consider maintenance as well. Technologies were divided into five groups according to the area they would be applied to: pavement construction, marking, inspection, maintenance, and work zone safety. Some of the ideas presented in the Tech Scan englobe a single technology from a single developer while other present several products from several vendors in the same activity. For Task A, the tech scan focused on identifying potential technologies with a brief description involving status, safety, and productivity aspects. Table 1 and Table 2 present a summary of the technologies identified in the tech scan.

Table 1: Identified remote-controlled technology for pavement construction, marking and inspection Area

Activity

Technology

Reference

DOTs involved

Research Institution

Vendors/ Developers

Used in PA?

Safety Improvement

Status

Construction

Several

Automated Machine Guidance (AMG) and Control (AMG)

[9-12]

ODOT and others

Several

Reduce exposure of on-foot workers to heavy equipment

Commercially available; in use

Construction

Mostly Earthwork

Fully Automated Machine Robots

[13]

None

Several

Reduce exposure of on-foot workers to heavy equipment. On-vehicle workers can operate equipment from a safer distance.

Commercially available; no testing

Construction

Project Layout and Stakeout

Robots and Unmanned Aerial Systems (UAS)

[14]

None

CIVROBOTICS

Reduce exposure of on-foot workers to traffic and heavy equipment

Commercially available; no testing

Construction

Demolition of concrete and asphalt

Demolition Robot

[15]

Unknown

None

Brokk and others

Not sure

On-foot workers can operate demolition devices from a safer distance

Commercially available; in use

Construction

Land Surveying

UAS

[16]

KDOT

K-State

Several

Not sure

Construction

Earthwork Volumetrics

UAS

[17,18]

Unknown

Several

Not sure

Markers Installation

Automated Marker Placement

[19]

Unknown

FHWA ARA

Stay Alert Safety Services, Inc and others

Not sure

Reduce exposure of on-vehicle worker to traffic and to high-temperature materials

Commercially available; in use

Inspection

Asphalt Density

Remote Controlled DPS for Asphalt Density Inspection

MnDOT

Reduce exposure of inspectors to traffic and heavy equipment

In development; conventional DPS is commercially available; in use

Inspection

Several

Integrated GPR and IR NDT Vehicle

[20]

CALTRANS

AHMCT

Reduce exposure of inspectors to traffic

In development; in testing

Inspection

Construction Inspection Records

E-Construction Inspection

[21]

LADOT

Headlight and others

Not sure

Reduce the time that inspectors are exposed to traffic and heavy equipment

Commercially available; in use

Inspection

Inspection of Conduits

Robots and UAS

[22]

ODOT

OU ARA

Several

Not sure

Reduce the need for inspectors to access conduits and exposure time

Commercially available; in use

Inspection

Data Collection

Remote Data Collection

[23]

None

FHWA

Inspectors and others can follow inspection vehicles from office

In development; initial testing

Inspection

Asphalt Silo Inspection

Silobot

[24]

None

Astec

Not sure

Reduce the need for inspectors to access asphalt silos

Commercially available; in use

Inspection

Asphalt paving temperature

[25, 26]

TxDOT MnDOT

Texas A&M MnDOT

Several

Not sure

Inspectors can assess and evaluate data from as safer distance

Commercially available; in use

Inspection

Several

[27, 28]

None

ARA Lab

Inspection

Concrete Maturity

Wireless Maturity Systems

Unknown

Several

Not sure

In development; initial testing Commercially available; in use

Inspection

Photography and Videography

UAS

Several

Not sure

Inspectors can operate device from a safer distance Inspectors can collect data from a safer distance Reduces on-foot worker exposure to traffic; worker can operate device from a safer distance

Pavement Marking

Automated Real-Time Thermal Profiling for Asphalt Paving (IR) Robot for General Inspection

[16]

Reduce the time that surveyors are exposed to traffic; Surveyor can operate device from a safer distance Reduce the time that surveyors are exposed to traffic; Surveyor can operate device from a safer distance

Commercially available; in use Commercially available; in testing

Commercially available; in use

Table 2: Identified remote-controlled technology for pavement maintenance and work zone safety Activity

Technology

Reference

DOTs involved

Research Institution

Vendors/ Developers

Used in PA?

Safety Improvement

Status

Maintenance

Pavement condition and distress

UAS

[29-32]

MassDOT and others

Several

Inspection workers can operate device from a safer distance

Commercially available; in testing

Maintenance

Roadside vegetation mowing

Remote-Controlled Mowers

[33]

CALTRANS

AHMCT

Several

Not sure

Reduce exposure of workers to traffic and rollovers accidents

Commercially available; in use

Maintenance

Crack and Joint Sealing

Seazall - Automated Crack Sealing

[34]

CALTRANS

AHMCT

Reduce exposure of on-foot workers to direct traffic

Successful prototype; not commercially available

Maintenance

Roadside Tree Removal

Telescoping Grapple Saw

[35]

CALTRANS

Unknown

Several

Not sure

Reduce the need for workers to climb and cut trees

Commercially available; in use

Maintenance

Road Litter and Debris Removal

Remote-Controlled Litter and Debris Removal - Several devices

[36-38]

CALTRANS, MnDOT

AHMCT

AHMCT, MnDOT

Reduce worker exposure to traffic and litter lifting and picking up

Maintenance

Culvert Cleaning

Robots for Culvert Cleaning

[39-41]

ODOT

Akron U

Several

Not sure

Reduces worker exposure to water, dirt and other hazards

The debris vacuum is commercially available; other devices not adopted Commercially available; in use

Impact vehicle

Autonomous Impact Protection Vehicle

[42-44]

CODOT, NDDOT, CALTRANS

Unknown

Royal Truck and Equipment and others

Removes danger for drivers operating impact vehicles

Commercially available; in testing

Area

Work Zone Safety

2.2.1 Construction 2.2.1.1 Automated Machine Guidance (AMG) and Control (AMC) Automated Machine Guidance (AMG) is a technology (onboard computers and position systems) that guides the equipment operator to better position the equipment in horizontal and vertical directions. Automated Machine Control (AMC) is a step further than AMG where the guidance computer steers the equipment, requiring minimal or no input from the operator. These systems have been used by some US department of transportations and their contractors as well as abroad [9-12]. The systems can be used for grading, compaction, milling, and paving equipment. Equipment can be purchased or retrofitted with the systems from multiple vendors. Figure 1 provides an example of the system flow using different equipment all with AMG. Both systems present many benefits like substantial increase in accuracy, productivity and quality of the work, continuous 24-hour work, minimization of human error, real-time collection of quality data, and others. The combination of all of these benefits results in a significant reduction in overall costs. Concerning safety, the use of automated machine systems reduces the need for on-foot workers directly in the work zone and in close proximity to heavy equipment. The main difficulty for AMG and AMC adoption is the need for 3D Design models, perception of high cost, training of personnel, and lack of specifications and inspection procedures [11].

Figure 1: Pavement construction flow using AMG equipment [11] 2.2.1.2 Fully Automated Construction Equipment In a next stage of construction equipment development, a fully automated construction equipment does not require an operator. The operator can control the equipment from a safer distance, or the equipment can be left working non-stop at the work zone using artificial intelligence systems. Most robot equipment is focused on earthwork movement. Companies like Built Robotics promote their own equipment while SafeAI can retrofit existing equipment to turn them into self-operating robots. Fort Robotics, on the other hand, provides remote-controlled equipment (Figure 2). For safety concerns, fully automated construction equipment would reduce hazards and improve safety of not only on-foot workers but also of equipment operators. No independent testing reports of this equipment was found.

Figure 2: Remote controlled earthwork equipment [13] 2.2.1.3 Robots and unmanned aerial systems (UAS) for Project Layout and Stakeout The company CIVROBOTIS develops robots and drones (Figure 3) for road project layout and stakeouts. Once uploaded with coordinates, the robots will mark all layout points and lines. The drone can provide 500 stakes per day. The company is an early-stage venture backed construction tech robotics start-up from Tel-Aviv with operation in San Francisco, CA. For safety, using the robot or drone would allow on-foot workers to conduct layout operations in from a safer distance decreasing direct interaction with traffic and heavy equipment. No independent testing reports of this equipment was found.

Figure 3: Robot and drone for road layout and stakeout [14] 2.2.1.4 Demolition Robots Automated or remote-controlled demolition equipment have been used in the construction industry for some time. However, the industry has advanced the technology greatly 9

over the last 20 years. Companies like Brokk present a series of remote-controlled demolition robots that can be used to remove concrete and asphalt from strategic locations in pavements and bridges where excavators of any size are not recommended as seen in Figure 4. In addition, traffic interruptions are limited due to the smaller features of the equipment. The use of this type of technology can improve safety for on-foot workers operating hand demolition devices which can cause several health hazards and also provide safer distance from the demolition work.

Figure 4: Example of bridge deck demolition using a remote-controlled robot [15] 2.2.1.5 UAS for Land Surveying UAS equipped with professional cameras can be used for area surveying instead of traditional surveying devices like Global Positioning System (GPS) and total station. The images and videos collected must be analysed by a 3D software. While traditional surveying usually covers about 3 to 4 hectares a day, UAS can survey about 10 times more in around 60 minutes [16]. This results in less on-foot workers conducting surveys near highways and construction zones and considerably less time of exposure for the UAS operators. However, using UAS for surveying has its challenges like vegetation and UAS current regulations on flying over traffic. 2.2.1.6 UAS for Earthwork Volumetrics Correct earthwork estimation is a key project aspect from design to construction. On site measurements of actual volumes is time-consuming, expensive, and sometimes dangerous depending on the topography of the area. As part of land surveying, photogrammetric data 10

captured by UAS can be used to calculate earthwork and stockpile volumes. Several studies have been conducted in this area in the US and abroad. Most studies report minimal error in comparison with more traditional methods like GPS and Light Detecting and Ranging (LiDAR) without UAS. The success of the measurements depends on the post processing of the images to build the 3D models [17, 18].

2.2.2 Pavement Marking 2.2.2.1 Automated Marker Placement Traditional marker placement methods require a worker to manually install the marker on the pavement (Figure 5). This exposes the worker to traffic and high-temperature adhesives. Automated marker placement systems install the marker without exposing the worker who remains inside the vehicle controlling the device (Figure 5b). In initial tests, comparative results showed that the automated system matched productivity and quality of high-end marker teams [19].

(a)

(b)

Figure 5: Marker placement with (a) manual method and (b) automated system [19] 2.2.3 Inspection 2.2.3.1 Remote-Controlled Ground Penetrating Radar (GPR) for Asphalt Density Uniform and adequate asphalt compaction is critical for pavement performance. Minimal reductions in asphalt density can cause huge impacts on pavement service life. Conventional quality control for asphalt density involves in-situ random determination of density from cores or 11

nuclear density gauges. Both methods are costly, labour intensive, and not necessary representative of the overall compaction quality. To overcome these issues, research has been focused on non-destructive methods that can evaluate asphalt density in a larger portion of the pavement like the Density Profiling System (DPS) with successful results. Currently, the Minnesota Department of Transportation (MnDOT) is leading a pooled program to advance the use of DPS for Quality Assurance activities for asphalt pavements. The team has also developed a prototype of a DPS that can be remote-controlled from a safer distance. In this way the inspector would no longer need to be on the pavement in close contact with traffic and other equipment.

2.2.3.2 Integrated GPR and Infrared (IR) Imaging in Non-destructive Testing (NDT) Vehicle NDT vehicles that can collect data at highway speeds have been used for some time now in the paving industry. Besides the benefit of limiting traffic interruption and continuous data collection, these vehicles also protect inspectors since they are not required to exit the vehicle to collect data on-foot. As an upgrade to these systems, The Advanced Highway Maintenance and Construction Technology (AHMCT) Research Center from the University of California, Davis proposed an integrated NDT vehicle with a typical 3D GPR system along with infrared cameras for thermal imaging [20]. 2.2.3.3 E-Construction Inspection Many activities performed during work inspection for the paving industry still uses a paper-based process. The advances in mobile technology can facilitate and expedite inspection records prompting fast sharing of information between inspectors and workers. Recently, the Louisiana Department of Transportation and Development sponsored a project using a econstruction inspection system called HeadLight [21]. Results indicate a 28% increase in productivity for inspectors when developing daily work reports among other benefits. While adopting e-construction inspection does not directly improve inspector safety, the fact that inspectors can effectively resume work in less hours, avoiding losing data and therefore having

to redo the work suggests that inspector would spend less time expose to traffic and other dangers. 2.2.3.4 Remote-Controlled Inspection of Conduits Conventional inspection of highway conduits requires an inspector to enter the conduit in order to evaluate the drainage conditions. This can be dangerous due to uncertainties regarding the conduit structural condition, water and sediments in different levels, and the possible presence of contaminants. In addition, a good portion of conduits are inaccessible due to their small diameter or obstructions (water, sediment, and others). This means that a great number of conduits are left uninspected losing appropriate drainage functions. In the past 15 years, the paving industry has used remote-controlled technologies to evaluate small diameter conduits. Since then, new and emerging technologies have been developed to inspect all types of conduits. Recently, the Ohio Department of Transportation (OHDOT) sponsored a tech scan research project to evaluate remote technologies for inspection of conduits [22]. They reviewed several robotic technologies (crawlers) that can access and evaluate conduits with diameter ranging from 12 to 120 in (Figure 6a). They also looked into UAS that can perform evaluation of conduit extremities and access larger conduits removing the need for an inspector to approach the structures (Figure 6b).

(a)

(b)

Figure 6: Conduit remote inspection using (a) crawler robot (b) UAS [22]

2.2.3.5 Remote Data Collection Due to the COVID-19 pandemic restrictions on workers sharing close spaces, the Federal Highway Administration (FHWA) developed a new methodology for data collection and human factor studies in a way that only one person (driver) is in the data collection vehicle. All other inspectors and interested personnel can stay in the office and follow the analysis remotely by a system mounted with cameras and different sensors in the vehicle (Figure 7). The current technology is in testing and demonstration phase and is primarily focused on human factor studies. However, there is potential to further develop the system to pavement inspection vehicles. This would lead to a significant reduction in the number of people in the vehicle while also opening the possibility of more people participating in the operation simultaneously than the vehicle would normally allow.

Figure 7: Remote data collection system [23] 2.2.3.6 Robot for Asphalt Silo Inspection Inspection of asphalt silos is primordial for safe storage and effective production of asphalt mixtures. Conventional inspection often requires an inspector to enter the silo to evaluate the structure involving serious risks. The company Astec recently developed a robotic system 14

(Silobot – Figure 8) to remotely inspect asphalt silos. According to the company, a silo inspection using the robotic system takes less than two hours to inspect all silo welds while also providing video, photographs, and other information. A conventional inspection takes much longer and only inspects the bottom third of the silo. No independent testing reports of this equipment were found.

Figure 8: Robot for silo inspection [24] 2.2.3.7 Automated Real-Time Thermal Profiling for Asphalt Paving Over the past 20 years, infrared cameras have been used to measure temperatures in newly paved asphalt pavements to inspect and avoid thermal segregation. More recently, these camera systems were attached directly in the paver (Figure 9) in a way that both the operator and on-foot inspectors can have real-time access to thermal data as the paving progresses. The Pavement Mounted Thermal Profiler (PMTP) is an example of this technology that was initially developed by the Texas Department of Transportation (TxDOT) in collaboration with Texas Transportation Institute and is now commercially available through the company MOBA [25]. Besides the quality and inspection improvements regarding thermal segregation, automated data collection accoupled into paving equipment reduces exposure for inspectors. The inspector can receive and analyse the thermal profiling from a safer distance. MnDOT also has experience with this type of technology.

Figure 9: Infrared sensors attached to paver for real-time thermal data acquisition [26] and the latest version of IR temperature scanners [27] 2.2.3.8 Robots for Inspection The Advanced Robotics and Automation (ARA) Laboratory from the University of Nevada, Reno developed a series of robots for inspection of bridges and bridge decks. The robot is coupled with several sensors capable of acquiring different sets of data. The robots have demonstrated successful mapping of surface cracks, detection of subsurface objects, and thickness measurements in case studies [28]. As Figure 10 shows, employing the robot allows inspectors to perform activities from a safer distance, drastically reducing exposure to passing traffic and equipment. While the robots developed by ARA are mainly focused on bridge inspection, several of the features can also be applied for pavements.

Figure 10: Robot for bridge deck inspection [29] 2.2.3.9 Wireless Maturity Sensors Traditional data collection for maturity analysis requires that inspectors connect the sensors (embedded in the concrete slab) to a portable computer. This means that the inspector is usually in close proximity to heavy equipment and traffic. A simple advance for this procedure is to use wireless maturity sensors with which the inspector can extract temperature data using a held-hand device from a safer distance.

2.2.3.10 UAS for Photography and Videography Road construction and inspection requires documentation of activities, pavement distresses, accidents, damage to equipment, and others. For most, if not all of these documentations, photographs, and video evidence are expected. Currently, most of this evidence is obtained from cameras and cell phones where the worker or inspector must be close to the point of interest which, for most activities, implies proximity to traffic and heavy equipment. Using an UAS the inspector can stay at a safer distance while capturing high quality images and videos from different angles that would not be possible by handheld devices [16].

2.2.4 Maintenance 2.2.4.1 UAS for Pavement Condition and Distress Analysis The collection of pavement condition and distress data is arguably the most common activity in maintenance plans for pavement assets. These surveys are usually done by visual observation with on-foot or on-vehicle inspectors or by automated data collection in a vehicle with a driver. Both methods imply in overlong exposure of inspectors and drivers to traffic. The US Corps of Engineering recently developed a study to evaluate the feasibility of using UAS and LiDAR technology to evaluate roughness (Boeing Bump and International Roughness Index - IRI) of airfield pavements [30]. Results were positive to mixed with concrete surfaces yielding poor results but indicate potential for the technology further development. For highway evaluations, researchers in Spain reported an error of 5% between their proposed methodology using UAS data and conventional IRI profilometer in a single case study [31]. The UAS images were more successful in the detection of cracks, covering a whole airport runway in record times [32]. Cracking detection was also pointed out as the best feature of UAS by a report from the Massachusetts Department of Transportation (MassDOT) [33]. 2.2.4.2 Remote-Controlled Mowers Management of roadside vegetation is also one of the most common pavement maintenance activities for the paving industry. Vegetation control is necessary to improve visibility, reduce potential and severity of roadside fire, and give a pleasant, aesthetic view for users. Conventional roadside vegetation control is conducted using tractors and mowers, but mostly on-foot workers with hand-held cutting tools. This type of work exposes workers to traffic and can be especially dangerous in steep slopes due to rollovers. Recently, the California Department of Transportation (Caltrans) sponsored a project to evaluate remote-controlled mowers for roadside vegetation management [34]. The research concluded that in difficult mowing operations like slopes (Figure 11) and confided areas, the remote-controlled mowers are far more effective than conventional mowers or string trimming. This in combination with the safety improvements compensates the higher cost of remotecontrolled mowers according to the authors. 18

Figure 11: Remote-controlled mower in a slope [34]

2.2.4.3 Automated Crack/Joint Sealing Longitudinal crack or joint sealing is a key activity for pavement maintenance. Cracks and joints without sealing will allow water to infiltrate the pavement and carry incompressible materials compromising pavement life. Traditional crack sealing is usually performed in a manual approach exposing workers to on-going traffic and hot bitumen sealing materials. AHMCT on a project sponsored by Caltrans developed an automated sealing equipment called Sealzall [35]. The Seazall system for heating up the sealing material and sealing arm is fully automated and can be controlled by a single operator inside the vehicle (Figure 12). Reports show a boost in productivity for sealing of longitudinal joints and cracks in jointed plain concrete and asphalt pavements indicating annual savings of four million dollars (2014). However, the last update in the machine mentions some issues with operational temperatures. Personal communication with AHMCT informed that the prototype was well-received by Caltrans and there are discussions into moving the system to commercialization.

Figure 12: Longitudinal sealing machine [35] 2.2.4.4 Remote-Controlled Tree Management Another part of vegetation control for roadside management is the removal of dead or invading trees along the road. Usually this is conducted by having crews climb the trees and cut them down, an activity that is inherently dangerous on many levels. Recently, Caltrans started to use and promote remote-controlled technology for the removal of trees like the telescoping grapple saw [36]. The device (Figure 13) grabs, cuts, and safely brings the tree to the ground for chipping. Besides safety benefits, Caltrans officials report high productivity. No independent testing reports of this equipment was found.

Figure 13: Telescoping grapple saw for roadside tree removal [36]

2.2.4.5 Remote-Controlled Roadside Litter and Debris Removal Roadside litter is a constant maintenance activity for most highways. Roadside litter is usually removed in two stages: collection of litter into litter bags and removal of litter bags into a collection vehicle. On the second task, workers exit the collection vehicle, grab the litter bags, and throw them in the vehicle. This type of work, besides being unproductive, exposes workers to on-going traffic and can cause injuries due to lifting of heavy bags and other larger items. Some transportation agencies and research institutions tried to come up with automated and remote-controlled solutions to collect debris and litter in a safer and more productive way. In 2006, Caltrans again with UC Davis developed the automated roadway debris vacuum, a selfcontained vehicle-vacuum system that removes debris and small litter from the roadside [37]. The vacuum is remotely operated from inside the vehicle using a joystick control. The operator can access difficult areas like behind guard rails, depressions, and bushes (Figure 14). Safety improvements are based on lower exposure of workers to traffic since the operator stays inside the vehicle and reduced injuries from bending and lifting in comparison to manual litter pick-up. The project estimated savings of $122 per mile based on field tests. The prototype was licenced and commercialized by Clean Earth technologies. The same research initiative also developed an automated litter collector that can be attached to a collection vehicle eliminating the need for workers to access the roadside on-foot (Figure 15). Performance reports indicate a good productivity of the device but recommends several modifications to improve it [38]. The device, however, does not address the first operation of litter removal (collection of small litter into bags). ACHMT, in a personal communication, informed that the device was considered not ideal for DOT use. Also, in 2006, MnDOT developed a trash-harvesting machine that uses a pickup unit lifting the trash through a combination of tines and a rotary broom (Figure 16). The device was developed to collect trash and litter from grassy areas in roadway shoulders [39]. However, the device failed to collect a satisfactory percentage of the trash in initial tests. MnDOT decided to not commercialize this particular product.

Figure 14: Automated Roadway Debris Vacuum [37]

Figure 15: Litter removal attachment operation [38]

Figure 16: Trash-harvesters prototype [39] 2.2.4.6 Robot for Culvert Cleaning Large culverts often get partially or totally obstructed by rock, dirt, and other debris impairing proper drainage. The removal of these significant volumes is costly, labour intensive, and dangerous as it exposes workers to water, dirt, and other hazards. The OHDOT conducted 22

research on remote-controlled cleaning devices for culverts and found that they were four times faster than using a vactor truck [40]. A similar performance was found by Caltrans [41]. The OHDOT report recommended the acquisition of the MicroTraxx remote controlled machine from Rohmac Inc (Figure 17). Hard-Line’s LP401 promises more productivity even though it was not evaluated by independent researchers [42]. Both machines can only perform cleaning in large culverts with a minimum of 4 ft height.

Figure 17: Two remote controlled culvert cleaning machines [41, 42] 2.2.5 Work Zone Safety 2.2.5.1 Autonomous Impact Protection Vehicle Impact protection vehicles are often the last protection barrier for on-foot workers and operators against invading traffic in moving work zones. Their job is to take the impact stopping vehicles from entering the work zone in full speed. However, one worker is still needed to drive the vehicle. In the case of an impact, the driver can suffer serious injuries with deadly outcomes. In addition, drivers are subjected to psychological trauma for operating in high-risk situations. The recent development of autonomous vehicles for several industries can provide a safer alternative for this important operation. Different DOTs have started to use autonomous impact vehicle for moving work zone operations [43]. Caltrans is testing and reviewing the Automated Truck-Mounted Attenuator (Figure 18) from the company Royal Truck and Equipment. Their final report is due at the end of 2021, but personal communication with AHMCT relates positive results.

Figure 18: Autonomous impact protection vehicle [44]

3 Selected Technologies The tech scan provided over 23 remote-controlled technologies that showed promising benefits to improve worker safety in highway construction, inspection, and maintenance. It was determined by the project’s Technical Panel that the technologies chosen for the next phase should have a priority to paving work and inspection. Therefore, technologies that were not directly related to pavement work like roadside maintenance, drainage inspection, and land surveying were disregarded from further analysis. The Technical Panel decided to farther investigate two technologies that are of substantial importance for asphalt pavement construction and inspection. The technologies selected focused on the quality control of asphalt paving. The first, Automated Real-Time Thermal Profiling for Asphalt Paving using Infrared Cameras, is able to determine the asphalt mat temperatures continuously during paving. Then, the Remote-Controlled Density Profiling System can provide quick and comprehensive density data for the asphalt layer during and after compaction. The final technology was chosen in leu of the country’s growing interest in connected and automated 24

vehicles. The Technical Panel choose the Autonomous Impact Protection Vehicle due to its potential for safety improvement of an activity directly related to work zone safety. Technology transfer workshops with leading experts and vendors were promoted for each selected technology. Field demonstrations for the DPS technology were also conducted. Appendix B presents a detailed account of the workshops. Video recordings of the presentations and discussions can be found in the IRISE website.

3.1 Thermal Profiling for Asphalt Paving Asphalt paving has a heavy reliance on temperature especially when initially laid and compacted. Asphalt concrete loaded at a lower temperature will deform less and behave more elastically than at a higher temperature [45]. This is especially critical during compaction. Compaction occurs during a specific temperature range to create the optimal pavement with high density but still retaining the desired air void content. Compaction of hot-mix asphalt typically occurs between 185 – 300°F [45]. As the asphalt concrete cools, the binder will become stiffer until compaction is significantly reduced. No significant compaction occurs below 175°F. If the entire asphalt mix is at too low of a temperature or if there is a significant temperature difference in certain areas, the pavement will not be properly compacted leading to lower density [46]. This leads to excessive air void contents which decreases the durability of the mix and allows for more air and water to penetrate the pavement creating potential for premature aging and moisture damage [47]. An initial air void content, typically between 6 – 8%, is achieved through compaction. After opening to traffic, regular traffic will farther compact the asphalt to the desired air content of 3 – 5% which will remain to the end of the service life [45]. For a dense graded HMA, just a 1% increase in air content from a 7% base can lead to a 10% decrease in the pavement life [47]. There are two key terms to consider when discussing issues with asphalt mix temperature: temperature differential and temperature segregation. Temperature differential is the difference in average mat temperature between a cooler area and the bulk area. Temperature segregation is the isolated areas of the mat that have a significant temperature difference [48]. A 2013 study used thermal imaging cameras to highlight cold spots, areas with a significant 25

temperature differential [47]. These cold spots can be seen in Figure 19 where they identified areas of temperature segregation at 212°F (100°C) where the surrounding areas were 285°F (140°C).

Figure 19: Temperature segregation identified with thermal imaging [47]

Temperature segregation has numerous causes but generally it is a result of differential cooling within the asphalt. There are many typical construction activities that can result in temperature differentials [48]: •

Asphalt mix cooling during transport

•

Lack of remixing before paving

•

Night paving with an ambient temperature lower than 70°F

•

Haul times of greater than 70 minutes

•

Accidental asphalt mix discharge on the base that is later paved over

•

Incidents that lead to paver stoppage Temperature segregation can occur in transport through loss of heat through the truck

sides and insulating cover [46,48,49]. The combination of improper insulation, long haul time, and low ambient temperature can cause significant temperature differences in transport [49]. Generally, any exposure to different environments for portions of the asphalt mix can cause 26

temperature differentials [46]. Temperature segregation can also be found in the accidental discharge of asphalt from a haul truck on the base surface to be later paved over. The dropped material will cool quickly and will not compact properly when fully paved over [48]. Asphalt pavement is one continuous material and therefore nonstop paving is preferred. However, mechanical or hauling issues can lead to the paver stopping for several minutes. Waiting too long between hauls can lead to a cyclic temperature segregation where the mat temperature has the potential to fluctuate up to 86°F (30°C) before paving continues [46]. This creates a strip of temperature differentials that may be significant enough to effect density if it is a long delay. A high-severity temperature segregation can reduce a pavement life by up to 50%. Cold spots in asphalt pavement causes nonuniform compaction which can lead to accelerated distresses (Figure 20) under traffic loading and environmental effects [47,48]. Common distresses that occur due to temperature segregation are oxidation and moisture damage caused by density differentials creating excess air and water exposure. There can also be a decrease in bond strength between pavement sections caused by the cooling caused by paver stoppage which can lead to transverse cracking [48]. Edges and joints are already especially susceptible to distresses due to the temperature differential created by being exposed to more air than just the surface [47]. This can result in a low density at the edges and joints, increasing the vulnerability of this location [49].

Figure 20: Distress due to temperature segregation causing inadequate compaction [50].

Taking care to reduce temperature segregation removes distresses related to the nonuniformity caused by the temperature differentials [51]. Quality control is needed to ensure proper asphalt temperature so that action can be taken if temperature segregation occurs. Recent advances in technology can be used to mitigate segregation to improve pavement performance. There are several traditional methods to determine asphalt temperature. Thermal couples have been used but are reported to have breakage issues and can only be placed in so many locations [47]. Temperature probes and gun-type thermometers are another common practice however these can only measure one point at a time [47,48]. These traditional methods are also performed randomly which does not guarantee temperature segregation will be noticed [47]. In addition, conventional methods require active presence of workers and inspectors directly in the paving area. That creates several safety hazards as workers and inspectors are close to heavy moving equipment, passing traffic, and hot asphalt concrete.

3.1.1 Proposed Technology 1: Automated Real-Time Thermal Profiling for Asphalt Paving Recent development in technologies can allow for more comprehensive profiling through high precision portable infrared thermal imaging [46]. Over the past 20 years, infrared cameras have been used to measure temperatures of newly paved asphalt pavements. Infrared is a method of evaluating materials based on their temperature to determine surface of subsurface defects [8]. Originally, this technology was used alongside the roadway creating images such as Figure 19. However, this method has a narrow field of vision, and the images must be merged together to see the entire project. There are also issues on accurately locating the identified problem areas to the correct location on the pavement surface [51]. To address these shortcomings, infrared technology has been farther used to provide comprehensive thermal profiles by attaching a scanning device directly on the paver as shown in Figure 21. This provides real time access of temperature data to the operator and on-foot inspectors. Inspectors can receive and analyze the information from a safer distance reducing exposure to hot materials and heavy equipment. This method of quality control provides nondestructive, diagnostic monitoring that provides quantitative and visual feedback. Infrared 28

imaging has been encouraged by researchers as a good quality control device to measure pavement uniformity [48].

Figure 21: Infrared sensors attached to paver for real-time thermal data acquisition [52,53]

Many Departments of Transportation (DOTs) have used a Paver Mounted Thermal Profiler (PMTP) from MOBA: Mobile Automation, initially developed with the Texas Department of Transportation and Texas Transportation Institute. This company has two systems that provide the full coverage thermal profile: the sensor beam and the Pave IR ScanTM, both shown in Figure 9. The sensor beam consists of 12 infrared sensors connected to a central MOBA OperandTM [26]. A GPS antenna is mounted above the computer to track location. The entire system can be powered by the paver if it is a permitted installation or by generator. The device is designed for easy set up and storage so that it can be moved from one machine to another quickly. The infrared sensors attached to the beam scan the area directly below the device and is compiled by the computer to output a complete thermal profile to the operator or inspector. The second, newer device is the Pave IR ScanTM. This device is attached to a mast along with the GPS and weather station and is connected to an on-board computer [52]. This device is can also be powered by the paver or generator and can easily be moved to another machine. The PMTP scans back and forth across the pavement to collect thermal data where is it

again compiled and shown on the computer for the operator. An example of what the operator or inspector would see for either set up is shown in Figure 22. Scanning devices temporarily store all collected temperature data automatically and can be stored permanently. The GPS location, paver speed, and any stops are also recorded. After data collection, the project can be transferred through USB and analyzed using the Pave Project ManagerTM software. This allows the contractor or inspector to view the project in more detail reducing the need to be inside the work zone. Some diagrams included in this software include time diagram for paver stops, quality factor, speed, and weather. The software also provides a quality control and quality assurance report specific to various DOT specifications. Figure 23 shows an example of two detailed diagrams automatically generated by the software.

Figure 22: On-board computer output for real time feedback [53]

Figure 23: Examples of Pave Project ManagerTM detailed reports with temperature profiles and paver speed or time diagram [53]

3.1.1.1 Advantages and Disadvantages The primary advantage of using a paver mounted thermal scanner is that it provides real time temperature profiles that allow operators to make immediate decisions that improve the quality and performance of the pavement. This reduces premature cracking and raveling later in the pavement service thus reducing maintenance costs. Since it is attached to the paver, a continuous temperature profile along with paver and placement details can be obtained instead of randomly selected areas like previous technologies. The systems are also easy to install and can be adaptable to many different pavers [53]. Previous technology relied on a distance wheel to determine location on the pavement relative to the thermal image or scan irrelevant to whether it was roadside or attached to the paver. The PMTP system is equipped with GPS technology so that the precise location of the pavement area in question can be accurately located, monitored, and repaired if need be. The automated data collection also reduces exposure to inspectors [47]. The inspector can receive and analyze the temperature profile from a safer distance. Continuing the safety aspect, this system does not need a dedicated operator and remains out of the way to other workers. The Pave Project ManagerTM software also has several advantages. As mentioned before, the software provides the temperature profile and several types of diagrams for the entire project. It also creates DOT specific quality reports. Additionally, while the raw sensor data can be accessed if necessary, the software prescreens the data for the user [52]. The software cleans the data by removing temperatures collected within two feet from the edge, data at areas where the paver stops for more than 10 seconds, and any outsiders exceeding the minimum and maximum temperature that may be due to error [52]. The final analysis zone prescreened by the software can be seen in Figure 24.

Figure 24: Cleaned temperature profile [52]

In 2016, the PMTP system was demonstrated for several DOTs including Alabama, Alaska, Illinois, Maine, Missouri, New Jersey, North Carolina, Virginia, and West Virginia. They asked personnel from the DOTs and corresponding contractors for their comments on the PMTP system with an overall positive response as can be seen in a few of the comments below [52]. •

Improves communication between plant and paver personnel for material movement

•

Visual display allows operators to note which activities effect the temperature differentials

•

Good forensic tool to investigate nonuniform mat density

•

Allows contractors to immediately take action against a potentially underperforming pavement

•

Reduces the risk of contractors being penalized for undercompaction

•

Removes opinions from identifying mixture segregation

•

Provides full coverage inspection instead of random locations

•

Can be used to mediate disputes on uniformity

•

Reduces future maintenance costs by reducing premature cracking and raveling

One disadvantage of this technology is that it has not been fully tested for warm mix asphalt (WMA) which is required by Publication 408 in Pennsylvania [54]. Extensive research has been performed on temperature segregation for HMA but there is limited testing on WMA despite having similar properties [55]. WMA is produced and mixed at temperatures about 212 – 302°F (100 – 150°C) whereas HMA is around 250 – 375°F (120 – 190°C). WMA is typically compacted at least 68 – 86°F (20 – 30°C) lower than HMA. The PMTP system is advertised to work on HMA with little information on the ability to analyze WMA. However, a 2017 study used the infrared camera technology adopted by PMTP to identify temperature segregation in their study with success. Additionally, the same study determined different temperature segregation severity categories based on temperature differentials for WMA with a 4% air content [55]: •

No segregation: <37.4°F (<3°C)

•

Low-level: 37.5 – 46.4°F (3 – 8°C)

•

Medium-level: 46.5 – 64.4°F (8 – 18°C)

•

High-level: >64.5°F (>18°C)

The differing temperature differentials to cause the same temperature segregation severity between WMA and HMA may need to be considered and farther researched to improve analysis of temperature profile data and to improve specifications.

3.1.1.2 Past and Present Experience The increase in pavement performance has led to the adoption of this technology through regular use or having been researched by other DOTs and their respective research institutions. American Association of State Highway and Transportation Officials (AASHTO) has developed a standard for Continuous Thermal Profile of Asphalt Mixture Construction (AASHTO PP 80-20 2021 – [56]). This provides a template for standard thermal profiling using technology that is immediately behind the paver with the goal to improve quality control across asphalt paving. Categories were developed to establish the severity of temperature segregation in terms of

temperature differentials [56]. These categories were adopted by many state departments into their own specifications. •

None to Minor: 0°F – 25°F

•

Moderate: 25.1°F – 50°F

•

Severe: >50°F

Paver mounted thermal scanning has been incorporated into several state specifications. Texas Department of Transportation (TxDOT) began investigating temperature segregation and the possibility of including testing in their specifications in 2000 [57]. TxDOT began with thermal profiling requirements using thermal cameras or other imaging systems in 2012 which farther developed to include paver-mounted thermal imaging systems in 2015 [58]. TxDOT uses the PMTP system on their pavers and used webinars and construction demonstrations to introduce the new technology to contractors. This was met with wide success that was adopted quickly and enthusiastically by contractors, leading to an increase in higher quality asphalt pavements [57]. TxDOT adopted the AASHTO PP 80-20 severity categories and if the temperature differentials are significant and reoccurring, action must be taken to eliminate farther segregation or correct section of severe segregation. TxDOT also recommends marking areas of moderate and severe temperature segregation so that a density profile can be performed. Following the implementation of automated thermal profiling by Texas, Louisiana Department of Transportation and Development (LaDOTD) also studied the effect of temperature segregation to create specification recommendations that suggest actions to be performed for a range of temperature differentials shown in Table 3 [48]. Louisiana also recommends a thermal profile system that constantly records the temperature across the full width of the pavement [59]. Minnesota Department of Transportation (MnDOT) has recently included the paver mounted thermal profile method in a special provision on asphalt specifications [60]. Ohio Department of Transportation has included the thermal and density profile measurements in their specifications to work towards pavement uniformity [61]. They found that while the use of PMTP was not a complete fix for temperature segregation, this device helped improve long term pavement performance [61]. As of 2014, several additional states are 35

working towards incorporating temperature segregation requirements into their specifications including Washington, Oklahoma, Tennessee, South Carolina, and Georgia [26].

Table 3: Specification recommendations for LaDOTD [48]. Temperature Differential (TD) from Target Laydown Temp (°F) 0 to 50

50 to 75

Actions •

No action required

•

Require contractor to reduce TD to below 50°F

•

Require operation to stop if TD is not reduced

•

Measure field densities in affected area

•

Quality assurance cores may be taken from concerned area

Above 75

•

Require contractor to reduce TD to below 50°F

•

Require operation to stop if TD is not reduced

•

Quality assurance cores may be taken from concerned area

•

Require contractors to remove affected area if density fails

3.1.1.3 What is needed for PA implementation? In this report, the newest PMTP also known as Pave IR technology, Pave IR ScanTM, is considered for implementation in Pennsylvania. This set up is the least invasive, simplest, and most up to date technology. When implementing this technology in a new state, DOTs often began with webinars and construction demonstration projects to teach about the new technology, how it can improve operations, and how to interpret the results [62]. This allows the DOTs and their respective contractors to see the technology in action and have a professional explain and answer any questions. At this point, the DOT can determine if they want to implement the technology in their state. The entire set up costs $30,000 - $35,000 with the bulk of the cost coming from the 36

infrared camera and on-board computer. Installation for this set up take less than 2 hours and initial training takes less than 4 hours [52]. No regular maintenance is necessary, and calibration is performed once a year in a lab with a blackbody source with accuracy of ±0.9°F ±0.25% and a stability of ±0.2°F [51,52]. Changing the specifications and the accompanying research is the most time-consuming aspect of adopting this technology, especially with the Pennsylvania’s use of WMA. DOTs often implement the AASHTO specification outlined above in AASHTO PP 80-20 [56]. However, since WMA is most often used in Pennsylvania, additional research will be needed to create a new specification to identify the severity of temperature segregation. Besides the severity categories, other aspects of the specifications are common amongst the current DOT using PMTP. There are allowable amounts of each severity in several specifications. Sebesta 2012 describes how TxDOT implemented PMTP [57]. This report recommends 50% allowable moderate severity because they found that many locations with moderate classification were barely over the threshold between minor and moderate. These areas may not be significant enough to warrant action but should be observed for potential premature distress. The report also allows 2% severe temperature segregation accepting that there will most likely be some significant temperature differentials when the paver begins and ends for the day. They also included a recommendation for night construction that the thermal profile should continue with the same specifications, but the ambient temperature should be noted.

3.2 Ground Penetrating Radar (GPR) for Asphalt Density Research and industry best practices indicate that the long-term performance of hot mix asphalt (HMA) concrete is heavily reliant on the in-situ air void content/density of the compacted layer [63]. Key HMA characteristics, such as stiffness [64], strength [65], and dynamic modulus [66] have all been shown to correlate to air void percentage. Failure to meet compaction parameters can lead to premature pavement degradation including cracking, raveling, and/or oxidation [67]. This is especially prevalent in longitudinal joints which are regularly the weakest part of a HMA pavement. Most often separate lanes cannot be paved in tandem creating a cold joint. This creates an area of lower compaction at the joint when compared to the 37

centerline of the lane inviting premature pavement deterioration and failure resulting in costly maintenance and reduced service life [60]. A nationwide study determined that each 1% increase in air voids over a 7% base led to approximately 10% reduction in pavement life [68]. To address the potential for underperforming longitudinal joints, PennDOT was one of the first states to include a minimum value of 91% for the theoretical maximum density (TMD) in their 2020 specifications [69]. Traditionally, HMA compaction testing is conducted through cores. This method is destructive, expensive, time consuming, and has limited in coverage, with typical random sampling measuring only 0.003% of pavement area [70]. Another significant shortcoming of coring is the while these measurements are useful for post-construction analysis, they cannot provide real-time feedback during the compaction operation [63]. An alternative approach is the nuclear density method. This method is non-destructive, has data collection on the same day as paving, and provides immediate results. However, the use of radioactive materials requires a special license, specialized equipment, and extensive training as incorrect operation of the device can result in great harm for inspectors and workers. Both these conventional methods put crew members in potentially dangerous situations where they are in close proximity to live traffic and heavy equipment [71]. To address the shortcomings of conventional methods, ground penetrating radar (GPR) is being used to quantify compaction through creating HMA density profiles [72]. This technology is a form of non-destructive testing that has been used for several decades. Originally this was a handheld device used to scan areas of concern or for quality assurance to analyze the subsurface features of a pavement, such as layer thickness or properties [71]. However, the effect of surface moisture on measurements made the previous use of this technology inadequate for real-time density measurements. Algorithm developments have since made accurate density profiles possible which will be farther discussed later in this section [71]. The American Association of State Highway and Transportation Officials (AASHTO) has since published preliminary standard practice for using GPR as a means to calculate HMA density profiles [72]. GPR systems work by sending electro-magnetic (EM) waves toward a target and receiving reflections from the varying of electrical properties between sub-surface materials. The 38

reflection amplitudes and two-way travel time of the received EM waves are used to calculate and analyze dielectric constants that can be correlated with percent air voids [63, 71]. GPR technology comes in two main forms: ground-coupled and air-coupled. Air-coupled systems holds the antennas certain height off the ground instead of in direct contact making it the more commonly used method for calculating compaction of HMA [71, 73]. Figure 25 presents a diagram of a Pavement Density Profiler (PDP) process which uses an air coupled GPR system but a differing data processing method.

Figure 25: PDP instrument background principle of operation [73]. A Density Profiling System (DPS) is a recent GPR technology used for determining asphalt compaction in the field. A DPS is a non-contact, air-coupled GPR that has shown promise to supplement, and possibly eventually replace, most field coring activities [74]. DPS method is also beneficial for longitudinal joint density evaluation because it allows for continuous, real-time measurements. DPS operators have also suggested potential for this application to thicker layers [75]. The device is equipped with antennas that are suspended a short distance above the pavement. This device has been used attached to a human operated vehicle (Figure 26) or behind trucks and rollers. The continued innovations of this technology will be explored in this report as DPSs become remote controlled devices.

Figure 26: DPS (courtesy of MnDOT) 3.2.1 Proposed Technology 2: Remote-controlled GPR The remote-controlled GPR is an DPS that allows operators to control the device and inspectors to receive data from a safer distance avoiding dangerous conflicts with live traffic and construction equipment. The remote-control aspect of this technology is still under development by MnDOT with Figure 27 showing the latest prototype. Since this technology is upcoming, there is limited information on the impact of the remote-controlled aspects. However, conventional hand driven devices or those attached to the trucks or rollers have also received the same technological update being used in the remote-controlled DPS. While remote-controlled DPSs will have the advantage of safety, the improved accuracy of density measurements has also been fully studied and found to be advantageous.

Figure 27: A prototype of MnDOT remotely operated rolling asphalt density meter Specifically, within the DPS system, GOR sensors send and receive the EM waves while recording the amplitudes and frequency of the signals. The signal is processed using a concentration box to determine the necessary amplitudes and the on-board computer uses the amplitudes to compute the dielectric constant of the pavement in real time. Dielectric constant is a measure of a material’s ability to store electrical energy. In the case of pavement engineering, this constant can be directly related to air void content and therefore density. A low dielectric value means the pavement will have a higher air void content and therefore a lower density. As a reference, the dielectric constants of air and water are 1 and 81, respectively, meaning EM waves travel much slower underwater. Asphalt is typically between 4 – 8, depending on the air content. Ensuring accurate dielectric constants is crucial to predicting the air void percentage across the pavement. A slight change in dielectric constant can lead to a large change in air content. The AC surface reflection method is a popular way to determine the bulk dielectric constant of the HMA. It utilizes the ratio of the amplitude of the GPR signal reflection from air to the HMA surface, A0, to the incident amplitude (represented by the reflection from the metal plate), Ai. The dielectric constant of the surface is determined, using the Equation 1 [76]:

$ ' 1 + & '" ( # !! = # * '" 1 − &' (

(1)

The dielectric constant values determined from Equation 1 can be empirically related to the relative ratio of pore volume to the total volume of the newly placed HMA lift because air has a lower dielectric constant than the surrounding HMA components, and the aggregate type and volumetric proportion are typically uniform [77, 78]. This requires a regression analysis to be performed using cores from each new specific HMA mix location. The equation format shown in Equation 2 is the common relation with A and B being calibration coefficient and d being the measured dielectric constant. This method has been shown to have a good relation and provide reasonable estimated of air content and density.

'+, ./01!01 (%) = ' ! %& (

(2)

3.2.1.1 Advantages and Disadvantages Using GPR systems for pavement analysis presents many benefits due to its nondestructive character. A SHRP2 study demonstrated positive potential of DPS use in projects for providing real-time density feedback after final mat compaction. Yet, the project also pointed out several needed improvements in the procedure for data collection, processing, and evaluation. The study required multiple passes at various transverse locations withing a lane because the study used GPR system with a single antenna. This increased survey time while also limiting the coverage area for some locations [77]. Geophysical Survey Systems, Inc. (GSSI) recently developed a three-sensor version of DPS to address this issue. These additional sensors can be placed from 1 to 2.5 feet away to cover significant areas of interest [63]. The three-sensor DPS increases testing productivity by being able to cover joints and wheel paths in a single survey pass.

Using this updated version, a study done at Highway 52 near Zumbrota, Minnesota, was able to collect the compaction data using 3D GPR that was equivalent to taking over 1 million cores [79]. Using GPR systems also can provide safety advantages because data collection is much faster than coring and other methods. This allows workers to be removed from the presence of live traffic more often. A remote-controlled GPR would remove any need for a worker in the right of way. DPS also has collects compaction data for the entirety of the pavement surface which is beneficial for improved QA/QC in the field. Another strong benefit of DPS is the real time data evaluation. This provides knowledge of when and where additional roller passes are needed for immediate correction, or if there are any other great deficiencies [63]. Current disadvantages that arise from the use of air-coupled GPR technology include the following [79]: •

For layers < 1”, accuracy is affected by the underlying layer

•

For layers > 2.5”-3”, accuracy is affected by varying density gradients throughout layer

•

Effect of surface moisture

•

Change in aggregate source along pavement section

•

Temperatures below 40℉ The advantage of AC surface reflection method for determining dielectric constants is

that if the upper lift is sufficiently thick (thicker than 1.2 inches (30 mm)), then the measured AC surface reflection depends only on the properties of the upper layer [63]. However, this advantage only applies to a tight range of thicknesses. If the upper layer is too thick the results may not be representative of the entire layer [79]. Surface moisture has shown to be detrimental to results because it interferes with GPR signals. Research involving different types of GPR and algorithms is being investigated to overcome this [70, 80]. This shortcoming makes hand driven devices preferred to those attached to rollers as rollers wet the asphalt as they compact to minimize adhesion of the asphalt to the roller [80]. The dielectric constant relationship also depends on the aggregates used in the pavement. Any major deviations in aggregate will significantly reduce the accuracy.

Reaching an accurate correlation between the dielectric constant and the HMA air voids is one of the biggest challenges of DPS use. Examples of poor correlations are common in the technical literature. The beforementioned SHRP2 study reported a poor correlation between the DPS-measured dielectric constants and air void contents for one implementation project. However, other non-destructive testing like nuclear density and infrared have also shown poor correlation with density under inadequate circumstances. The study concluded that heavy rains that occurred in the asphalt mix plant and the use of an experimental fiber-containing mix might have contributed to the poor results [77]. A 2015 study also reported poor correlations between measured dielectric constants and core-measured air void. However, in this particular project, measured air void content varied significantly (3 to 4.6 %). This variation, besides the use of a thicker layer, might have contributed to the poor correlation according to the authors [81].

3.2.1.2 Past and Present Experience Past experience of using DPS to survey HMA air void content was compiled for a SHRP2 project in cooperation with Minnesota, Maine, and Nebraska DOTs [63]. This section summarizes the main findings of these field surveys conducted in projects representing a variety of construction techniques, HMA mix designs, and paving conditions and requirements. Highway 52 near Zumbrota, Minnesota This MnDOT project was divided into four test sections differed by mix design and number of compaction roller passes. Using DPS, the study was able to fully analyze the pavement surface producing millions of data points over the course of several weeks. In this study, results from the GPR dielectric data at the core locations were correlated with the air void measurements for all sections. Results of this survey produced mostly good correlations as seen in Figure 28 where the model explains almost 70% of variation in the air void content for Section 1. For these surveys, the data collection was taken on different days, different lanes, and at several weather conditions. In addition, the location of DPS measurements did not match the exact locations where cores were taken for some points. This testing variability can have an 44

effect on the dielectric response and overall correlation with air void content. When the coring and dielectric measurements are more coordinated, the correlation of the model improves as seen in Figure 29. 14.0% y = 11.472e-0.915x R² = 0.6941

Core Measured Air Voids

12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 4.8

5.2 5.4 5.6 RDM Measured Dialectric

5.8

Figure 28: Core Measured Air Void vs. Ground Penetrating Radar Dielectrics for Section 1 [63] 14.0%

Core Measured Air Voids

12.0%

y = 248.73e-1.518x R² = 0.8331

10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 4.8

5.2 5.4 5.6 RDM Measured Dialectric

5.8

Figure 29: Section 3 Correlation Model [63]

Using the physically based relationship between air void content and dielectric constant, the study also explored the potential of DPS or real-time compaction uniformity evaluation allowing the device to display measured dielectric constant in real time. This relationship indicates that HMA presenting lower or higher dielectric constant should have higher or lower air void content, respectively. In an example of this type of survey, air void content taken from cores at the same locations as the DPS measurements suggest a good correlation between the tests. In Figure 30, areas with higher dielectric constant locations indicate an adequate level of compaction while areas with lower dielectric values indicate insufficient compaction. The data forms a compaction map that provides real-time feedback to contractors and inspectors.

Core ID: W6 Low Core ID: W5 High Continuous Profile Allows Identification of High and Low Density Areas and Core Validation

5% Air Voids

10% Air Voids

Figure 30: Real-time data visualization and comparison with cores [63]

U.S. Route 1 Cherryfield, Maine This Maine DOT study was the first trial survey for DPS technology and was predominately for training proposes. The poor correlation between the DPS-measured dielectric constants and laboratory-measured air voids for one section were attributed to the lack of the 46

DPS measurements taken directly at the core location. In this survey, once core locations were selected, the DPS cart was used to resurvey the section and determine the core location. A total of five cores were collected. High and low dielectric cores were selected with the first survey section, and high, low, and medium cores were selected within the second section. While more cores are generally desired, the cores did cover a large range of dielectric values and produced a good model. Results indicate that performing the DPS measurements directly over the core is crucial to a satisfactory calibration model (Figure 31). Moreover, results indicate that exact core locations must be known even when using highly accurate GPS and distance encoders.

Figure 31: Cherryfield, Maine calibration model [63]

This section’s results indicate inconsistent compaction as large fluctuations of dielectric data were observed. There were a few regions of very low dielectric suggesting high air voids at both the pavement lane and joint. Average air void content estimated based on DPS dielectric constant was around 6%. However, both lane and joint presented areas of substantial air void 47

content up to 14.6%. Interestingly, data suggested that compaction at the wheel path was more consistent than other locations (nearly 1/10 of the variability observed at the lane and joint). This may have happened due to Maine DOT’s focus on wheel path compaction for QA/QC testing. Likely contractor practices evolved to produce high level wheel path compaction. However, the report suggests that future implementation of the DPS in Maine must include data from lane and joint as many studies have conclude that compaction in other parts of the pavement, especially at the longitudinal joint are essential for adequate pavement performance.

HWY 2 near Lincoln, Nebraska This survey was a training exercise in which the DPS measurement process and calibration with cores was demonstrated and explained to the Nebraska DOT. Compaction was considered satisfactory and mostly uniform for both areas in the center of the lane and at the joint. However, some points presented scarce areas of low dielectric constant indicating potential issues with compaction (Figure 32). Using DPS in active projects can help improve compaction these locations.

Figure 32: Joint survey [63]

HWY 9 near Clifton, Maine A DPS survey was conducted by Maine DOT in both the shoulder and in the mainline over all sections. To maximize the area of the surveys, the DPS was adjusted with a wider default sensor offset of 2.75 ft. Results indicated, similar to the other surveys described above, an overall uniform compaction. Shoulder and mainline presented similar dielectric values. However, adjusted left sensor consistently reported a lower compaction. The report does not mention a reason for this. Also, as seen in other sections a few locations of low dielectric were observed.

I-95 Near Pittsfield, Maine For this section, the study reports overall consistent compaction as estimated by the dielectric constant. Figure 33 presents the dielectric maps for segments 1 through 4 measured near the longitudinal joint. As known, joint compaction is often lower than the center of the lane. Results from this section demonstrated (Figure 33) the lower joint compaction as measurements 1.5 ft away from the joint suggested high air void content (consistent lower dielectric values) at the joint.

(a) Segment 2

(b) Segment 3 Figure 33: Dielectric Maps from Joint Surveys of I-95 near Pittsfield, Maine [63] HWY 14 near Eyota, Minnesota This was the most comprehensive field study conducted in the SHRP2 project resulting in the collection of nearly 130 miles of data. This field study incorporated lessons learned from the previous field surveys and used a more rigorous data collection procedure. Six sections, differed by the mix design or number of roller passes, were tested. This time, over 30 cores were used for the calibration model for the entire paving project (Figure 34) with a reasonable fit (R2 = 0.74).

Figure 34: Eyota, Minnesota calibration model for all cores [63]

Using the model developed above, the dielectric data were used to make real-time estimations of compaction performance. This information was communicated to contractors providing valuable information on the effect of roller patterns and mix changes in overall compaction. According to the study, contractor received the information positively showing potential to use DPS surveys for real-time compaction QA/QC. Table 4 shows a comparative example of mean and median values of estimated air void contents for each section joint and lane. Table 4: Mean and Median Air Void Contents for Mid-lanes and Joints [63] Section

Air Voids, Percent Lane

1 2 3 4 5 6

Median 6.61 5.70 6.25 5.75 6.31 6.67

Joint Mean 6.66 5.69 6.25 5.77 6.34 6.68

Median 7.73 6.31 N/A 5.91 N/A 6.79

Mean 7.7 6.3 N/A 5.95 N/A 6.79

Section 1, the control section with 3 rollers passes and 3/4-inch maximum aggregate size, showed the highest air void contents for both mid-lane and joint. Sections 2, 3, and 6 differed from the control section by number of roller passes (4), maximum aggregate size (1/2-inch) and presence of Evotherm, respectively. Adding a fourth roller in pass in Section 2 significantly improved compaction in the mainline and at the joint. The effect of decreasing maximum aggregate size in Section 3 showed less significant effects on compaction. Adding Evotherm to the mix in Section 6 improved only the compaction of the joint. Section 5, similar to Section 3 but with the addition of Evotherm, also showed little improvement of compaction in the mainline. Section 4, a combination of Section 2 and Section 3 (additional roller pass and ½-inch maximum aggregate), resulted in the best overall compaction reaching similar compaction of Section 2’s mainline but substantially improving compaction at the joint. 3.2.1.3 What is needed for PA implementation? Before implementation of the remote-controlled GPR, PennDOT and contractors should implement conventional DPS. This will require testing and training of personnel with special focus on developing appropriate procedures for the calibration model of the dielectric constant regarding air void content of cores. A Maine DOT crew found PaveScan DPS easy to use [82]. For successful correlations between air void and dielectric constants, previous studies recommend the following practices [75, 77,83-85]: •

Conduct survey immediately after final paving.

•

Use HMA lift thickness is 1.5 in. or less.

•

Conduct GPR survey directly over the cores’ extraction location.

•

Cores should comprehend a full range of dielectric constants.

•

Temperature during survey should be greater than 1°C.

•

Minimize time between GPR survey and coring.

For the survey discussed in the past experience section, the following procedure was used [63]: 1. At the project site, set up and initiate the DPS device per the manufacturer’s instructions. 2. Calibrate the device using airwave and metal plate. Airwave calibration requires rotating the sensors or pivoting the cart so that sensors are lifted minimum of 2 ft from the ground. Metal plate calibration requires data collection over a metal plate. 3. Collect DPS data in 500-ft segments of HMA mat after finish rolling. 4. Select core locations and conduct DPS survey at these locations. 5. Determine laboratory-measured air void content data from cores 6. Conduct calibration of the relationship between air void content and dielectric constant. In one of the surveys conducted in a 2017 study, issues with the coordination between the paving and the survey reduced the number of cores the team was able to collect. This resulted in the following recommendations to avoid rushing during DPS surveys [63]: •

Implement DPS surveys on projects with full traffic closure until the DPS and core collection team are more experienced.

•

Ensure an adequate number of personnel for coring and DPS survey.

•

Perform survey with just one pass only in the most critical compaction area.

•

Perform additional surveys without coring (real-time compaction will be provided based on established relationship between air void content and dielectric data). PennDOT publication 408 specifies the use of cores by lot for compaction QC for both

the joint and the mat. For quality control strip of the joint, the publication requires five 6-in cores randomly selected for density testing. These cores can be applied for calibration of the RMD dielectric – air void content model. For determining optimum-rolling pattern, the publication allows the use of conventional non-destructive testing. Density can be determined using an approved nuclear gauge according to PTM 402, or with an approved electrical impedance gauge according to PTM No. 403. The publication lists the following gauges as approved for density testing [54]: 53

•

Troxler Electronics, Model 3411B or Model 4640B

•

Campbell Pacific Nuclear, Model MC-2

•

Seaman Nuclear, Model MC-2

•

TransTech Systems, Inc., PQITM, Model 300 or Model 301

•

Troxler Electronic Laboratories, PaveTrackerTM Therefore, the initial implementation of DPS can be focused on determining optimum

roller patterns and then, considering acceptance and improved testing procedures, for QA/QC.

3.3 Impact Protection Vehicle for Work Zones Pavement construction, inspection, and maintenance are mobile operations that often require an active on-foot and on-equipment crew directly on the highway lanes in close proximity with passing traffic. In addition, these mobile operations are notoriously slow when compared to the passing traffic in speed differences that can reach over 50 mph. This combination of mobility, low speed, and proximity to passing traffic subjects highway workers to many risks. A 2021 study points out that in 2017 over 60,000 injuries were reported due to a total of 158,000 vehicles crashing into work-zones in the US [86] - many of these involving state Department of Transportation (DOT) workers [87]. Several safety actions in training, design, and technology have been applied to reduce the number and severity of accidents cause by passing traffic entering the work-zone increasing safety for both workers and the public. One of most successful of these interventions was the adoption of Impact Protection Vehicles (IPV) also known as shadow vehicles. The idea is that during mobile operations, the protection vehicle will follow the work zone acting as a barrier between the passing traffic and the on-foot and on-equipment workers. Recently, IPVs have been deployed with attached impact attenuators acting as a cushion for the impact, popularly called truck mounted attenuators (TMA). It is recommended that the attenuators should be installed on a relatively heavy vehicle. Texas and Missouri DOTs, for example, require the host vehicle to weigh at least 16,000 lbs and 20,000 lbs [88], respectively. The latter number is also the recommendation in Sweden [89]. The 54

concern about the IPV weight resulted in a project by the Texas DOT on trailer-mounted attenuators since the trailers and tractors are lighter than regular IPV. The research concluded that heavier vehicles are still preferential for this type of operation and that the Texas DOT should maintain the current 20,000 lbs requirement [88]. While using IPV provides a great safety benefit for workers, the IPV driver remains in harm’s way. Swedish researchers conducted impact tests on three different attenuator-IPV combinations including a TMA on a tractor (Figure 35), a TMA on an articulated front-end loader, and a TMA on a trailer demonstrating the dangers of an impact to the driver [89]. Using a lighter IPV like a tractor can cause severe neck injuries to the driver.

Figure 35: Impact testing of TMA on a tractor [89] Usually, errant vehicle impacts are head-on and cause the IPV to accelerate forward [88]. Initially, the support of the seat and headrest will restrain the driver from flailing rearward which is a generally less dangerous movement than forward movement. When assenting the risk of injury for the IPV driver, ridedown acceleration of the support vehicle is the recommended criteria [90]. It is known that the weight difference between the IPV and the errant vehicles is a key factor in the IPV accelerations [91]. The use of a heavier IPV is indicated to reduce the risk and gravity of injuries for the IPV driver [88]. Nevertheless, even when using attenuators attached to extra-heavy vehicles, serious damages can occur as seen in Figure 36 especially considering that the errant vehicle can be an 80,000 lb tractor-trailer traveling at 65 mph. In addition, there are concerns with the psychological harm that the IPV driver may suffer daily. 55

Figure 36: Accident involving IPV of the Virginia DOT [92] A news article from PennDOT informs that The Department has hundreds of IPVs (over 60 attenuators mounted on trucks and over 300 on trailers in 2018) within its fleet in a variety of configurations: Truck Mounted, Scorpion Trailer Attenuator, and VORTEQ Trailer Attenuator [93]. Road crews prefer the attachable trailer attenuators because of the convenience of connecting the attenuator to any PennDOT dump truck with a trailer hitch. The truck mounted attenuators require a dedicated mounting structure be affixed to a truck taking several employees to attach and remove the attenuator from the truck which makes it a less popular solution. A PennDOT and Pennsylvania Turnpike Commission Request for Information for Highly Automated Work Zone Vehicles from 2019 informs that since 2015, the Pennsylvania Turnpike Commission has experienced over 90 truck mounted attenuator (TMA) crashes along the Pennsylvania Turnpike. In 2017, 18 PennDOT owned attenuators were impacted in work zones [94]. These numbers show the extreme risky environment that a IPV driver faces daily at work.

3.3.1 Proposed Technology 3: Autonomous Impact Protection Vehicle Although, the advancement of Connected and Autonomous vehicle (CAV) technologies has grown exponentially in recent years, it is still potentially years away from wide application to travel for the general public. Nonetheless, the use of this technology in specific and controlled areas like IPV is receiving attention from many State DOTs. The autonomous impact protection vehicle (AIPV) also commonly known as autonomous truck-mounted attenuator (ATMA) vehicle is an automated version of IPV in which the driver is removed from potential harm way as the IPV is controlled from a distance. The AIPV system consists of a leader truck (LT) which guides the follower truck (FT) which is usually installed with a truck-mounted attenuator (TMA) as seen in Figure 37. An autonomous leader-follower system composed of actuators, software, electronics, and vehicle-tovehicle (V2V) communication equipment enables the FT to drive autonomously following the LT. The system, using a navigation computer, records velocity, heading, and position information of the human-driven LT to then transmit the information to the unmanned FT vehicle over a V2V communications link using data packages called “crumbs” as illustrated in Figure 38. These transmitted crumbs allow the FT to autonomously control speed and accelerations as well as control lateral movement at a programed gap distance [86, 95, 96].

Figure 37: AIPV system overview [95]

Figure 38: AIPV system layout [97] The first AIPV program in the USA was launched by the Colorado DOT in 2017 [98]. The positive experience led to an autonomous maintenance technology pool fund including 12 paid state DOT members [99]. The Pool Fund currently has 14 State DOT members, including Alabama, California, Colorado, Illinois, Indiana, Kansas, Minnesota, Nevada, North Dakota, Ohio, Oklahoma, Texas, Virginia, and Washington DOTs. The Missouri DOT awarded a contract to purchase two AIPV vehicles for work zone maintenance in 2018 [87]. The following year, the University of Tennessee Center for Transportation Research announced an AIPV pilot demonstration. In Florida, the University of Florida in collaboration with the Florida DOT is also initiating plans to evaluate AIPV [95]. Aside from these states, California. Minnesota and North Dakota have purchased AIPV systems and vehicles indicating that the interest for this technology is increasing fast among the country’s DOTs. The details of the initial experience of these states and research institutions with AIPV will be discussed later. 3.3.1.1 Advantages and Disadvantages As mentioned before, the main benefit of the AIPV system is removing the driver from harm’s way, which includes potential death or lifelong injury. According to a 2021 study, developers also point out other advantages of the AIPV [96]:

•

Removing psychological trauma: there is a psychological impact upon the driver since the worker is considered to be a “sitting duck” waiting to be hit. In addition, the fear of potential catastrophe also affects the worker’s family and community of support.

•

Repurposing of worker: with an autonomous process, the IPV truck driver can operate another vehicle in safer conditions or even be trained to monitor the autonomous vehicle’s operation from the leader truck. Since this worker has experience with moving working zones and IPV, they can effectively assess the performance of the AIPV.

•

Removing human error: automated vehicles maintain the designed gap distance with great accuracy; human drivers will often, unconsciously, vary the gap distance coming too close to their protected vehicle or work zone endangering them with a secondary impact. In addition, safety and trust can be improved because the AIPV will remain on track even in extremely dangerous situations. The human basic instinct for selfpreservation is removed with the driver. Moreover, the AIPV will automatically brake the vehicle upon impact, an action that sometimes is overlooked by human drivers.

•

Removing financial hazards: Using a AIPV eliminates insurance claims of lifelong injury, including huge medical expenses and worker compensation payments in cases of accidents mitigating liability. In addition, considering the 2016 US Department of Transportation “economical value of a statistical life” of 9.6 million dollars, if the AIPV system avoids a single death, the investment will be a huge financial success.

From the perspective of workers, the pool fund study developed an initial research into the perception and acceptance of AIPV by work zone workers. The study surveyed 13 workers from the Colorado and California DOTs on their experiment with AIPV. In total, 13 DOT workers responded to the survey. Based on worker level of AIPV experience, they were classified as High and Low experience. Results showed that, overall, workers have a positive acceptance of the AIPV technology indicating that it was a safety improvement, as compared to having a human driving the IPV. High experienced AIPV workers reported significant greater trust in AIPV when compared to less experienced workers suggesting that trust and acceptance increase with experience and proper training [95]. 59

Nevertheless, workers expressed concerns regarding reduction in crash frequency and project duration. Some workers also have doubts concerning the AIPV ability to safely change lanes and maneuver in horizontal and vertical curves as well as operation during several adverse conditions like poor visibility, adverse weather, and denser traffic volumes [95] AIPV are suited for fast-track technology development programs which are common in many US states due its deployment being based on highly controlled mobile work zone environments. State DOTs are encouraged to test the technology in a closed and controlled environment before real life situation environments [96]. Testing can be conducted following the criteria developed by a 2021 study in which the AIPV system’s performance is evaluated as follows [86]: •

Statistical indicators: For each test, the overall statistical indicators are selected and tested to assure that the system meets basic requirements of operation.

•

Probability Distribution: For each test, an analysis was made do disclose the probabilities of errors within a certain range.

•

Consistency: The performance must remain the same for different scenarios

Several tests based on different scenarios were conducted to evaluate the performance of an AIPV [96]. Results are in Table 5. Tests that achieved successful results are highlighted in green. Tests that were not consistent in achieving adequate results are in yellow. Failed tests are highlighted in red.

Table 5: Summary of AIPV testing results [96]

Another 2021 study also tested an AIPV system and concluded that it functioned as expected with an acceptable performance when compared with the designed criteria [86]. The authors tested communication loss, following distance and accuracy (Figure 39), obstacle detection, and emergency situations. The system also presented a good consistency when tests were repeated indicating a stable performance.

Figure 39: Frequency distributions histograms of the cross-track error in following distance and accuracy tests: (a) following accuracy, (b)lane changing, (c) roundabout operation, (e) minimum turn radius, (f) U-turn [86] A 2020 study evaluated the performance of the LT and FT in different scenarios regarding car-following distance, critical lane-changing gap distance, and intersection clearance time. For car-following distance results indicated a minimum of 75 ft for a LT and 30 ft for a FT. In terms of a lane-changing gap distance, the system needs a minimum time headway of 20 seconds for a 100 ft distance between LT and Ft to perform a safe lane change. If the distance is 62

doubled (200 ft) the headway time increases to 26 seconds. Concerning the intersection clearance time, for a gap distance of 100 ft or 200ft, the system requires 15 or 25 seconds, respectively, to cross an intersection (or to make a right turn). These numbers are significantly higher than for common passenger vehicles, according to the authors, indicating the need for extensive training of the AIPV operators and FT drivers [100].

3.3.1.2 Past and Present Experience In this section experience from other DOTs and research institutions are presented. The majority of this information was previously compiled for the pool study [95]. California The University of California, Davies have tested the AIPV in 2020 with 40 hours of operation time mostly on flat roadways. Although the testing was promising, the AIPV failed when tested under an overpass [96]. The AIPV was considered optimal for operations like sweeping, lane striping, and other moving mainline highway maintenance activities. The Advanced Highway Maintenance & Construction Technology Research Center (AHMCT) at UC Davies was contracted by Caltrans to evaluate AIPV. The report of this project should be available soon. Caltrans is also developing a training program for potential AIPV operators [95]. Colorado Colorado’s Department of Transportation (CDOT) reported approximately 110 highway test miles of striping operations with AIPV, through June 2019. CDOT is now testing the second generation Kratos system with a new paint truck. The second-generation system includes upgrades in cybersecurity, side view sensors, improved cameras, improved user interface, vehicle to vehicle communication, data storage, external human-machine interface (E- HMI), Astop, backup navigation, and operation in GPS denied mode. North Dakota

The North Dakota Department of Transportation (NDDOT) tested AIPV in the second semester of 2020. Testing was conducted on relatively flat low volumes road. Researchers reported successful operation regarding gap control, but some GPS connection issues that led to the loss of connectivity between the leader and the follower vehicle. However, these issues were solved during the testing [96]. Minnesota Kratos is also the system supplier for the Minnesota’s Department of Transportation (MnDOT) which is retrofitting an existing MnDOT truck with the system. MnDOT is interested in identifying procurement needs and delivery considerations, such as transport, insurance, schedule, and determining inspection requirements on AIPV components upon delivery and integration. Missouri The Missouri Department of Transportation (MoDOT) has worked through several stages of AIPV development initiating in 2017. Their system was also provided by Kratos for the AIPV technology in outfitted dump trucks. In 2019, MoDOT performed extensive testing for 32 hours. The main difficulties that the testing revealed were 1) reaching highway speeds by platooning the trucks, 2) following through both traffic signals and intersections, and 3) driving laterally offset from the lead vehicle. The department has reported that several issues identified during the initial testing were solved to finish the 32-hour testing. However, it is unclear which issues were identified and solved. Full initial deployment is expected in 2020 – 2021. Tennessee TDOT conducted an AIPV a pilot demonstration with twenty-four case scenarios to analyze the different operational components of the system [96]. In agreement with Caltrans, Tennessee DOT testing also concluded that the AIPV system is better suited for work zone operations that require continuous movement for longer periods of time like retracing or installation of pavement markings and roadway sweeping. For stop-and-go operations, like 64

pothole patching, weed spraying, and trash pick-up, researchers found that the tested AIPV system needs further developing and testing. In addition, the system denied locations when operating with Global Positioning System (GPS) for a short period of time. This issue was also mentioned by Caltrans, confirming that the tested AIPV system requires improvement for operations at locations with overpasses, heavy foliage, tunnels, and other locations that will induce long periods of sustained GPS signal loss. Additionally, due to the “e-crumb” following technology, the testing pointed to issues with the ability of the AIPV system to consistently shadow/protect the service vehicle at all times. In some situations, the autonomous follower vehicle will not immediately shift to the lane behind the service vehicle. Since the pilot testing in Tennessee, an offset functionality has been developed for the system. Researchers point out that extensive training and practice is crucial to ensure a safe operation of the AIPV system. Virginia The Virginia Tech Transportation Institute formed a consortium in 2018 for the development of AIPV. The program is divided in several phases covering design, demonstration, and testing in real operations. The program has concluded phase 1 which dealt with the design of the leader-follower system. International Experience: England England with Kratos system is also developing a program to test AIPV. The program comprehends three main stages: operation at Goodword, deployment on M4 highway, and finally unmanned operation. Initial operation on M3 highways was of 1,000+ miles. Researchers indicated several needed improvements like a more robust user interface, V2V Hardening for radio frequency (RF) interference, and robust obstacle detection to support cone lanes. Resolving these and other issues during testing resulted in GPS card upgrade, antenna upgrade, and configuration setting adjustment. The program moved into Stage 2 in November 2019. All issues identified during the British testing have been retrofitted into an existing system by CDOT, MoDOT, Caltrans, and MnDOT. 65

3.2.1.3 What is needed for PA implementation? In this Tech-Scan the AIPV technology is considered as a technology requiring additional development, but able to be implemented in the near future. The additional development is more related to definition of proper deployment for the AIPV along with the development of rigorous testing and training protocols. Below are some considerations for AIPV implementation in Pennsylvania: Acceptance of the technology In October 2018, Governor Tom Wolf signed Act 117 of 2018 into law. Act 117 allows for PennDOT and the Pennsylvania Turnpike Commission to implement Highly Automated Work Zone Vehicles in work zones. The 2019 RIF also shows increase interest for automated solutions for IPV in work zones [94]. PennDOT and the Turnpike Commission are involved along with other state transportation agencies and research institutions in several connected and automated vehicle initiatives like the PennSTART Test Track, the 2040 Vision, and the Statewide Connected and Automated Vehicle Strategic Plan [101]. Although these initiatives are more focused on traffic and transportation, they also mention the support for testing and interest maintenance and operations. This shows that there could be high interest and acceptance of AIPV within the IRISE community. Specifications and guidelines for deployment of AIPVs PennDOT Temporary Traffic Control Guidelines (Pub. 213) informs that any non-work vehicle can be used as a shadow vehicle (popular term for IPV) provided that the vehicle is equipped with flashing, oscillating, or revolving yellow lights which are visible from any direction. The publication also mentions that when the work zone is placed on or along freeways and expressways, the shadow vehicle must be equipped with a truck mounted attenuator (TMA). In this case, the shadow vehicle weight must be greater than the minimum weight as specified by the TMA manufacturer. TMAs are optional in other types of highways. The use or not of an 66

AIPV has implications on the necessity of some Temporary Traffic Control (TCC) devices and the distances from the TCC signs and the work zone [102]. On a more National level, concerning guidelines for deployment of AIPV, Parts 5 and 6 of the Manual on Uniform Traffic Control Devices (MUTCD) from the FHWA can be valuable indicators of the process [95]. Part 5 refers to signs regarding Autonomous Vehicles for work zones while Part 6 accounts for many different situations. According to the authors, Part 5 and Part 6 can be used for low and high-volume road, respectively. Despite MUTCD having useful general principles, the manual refrains from giving direct guidelines for AIPV. Researchers and practitioners have expressed doubts on when and where to deploy an AIPV. Another project of the beforementioned pool study involves coming up with reasonable guidelines for deployment of AIPV. The study will focus on what is an adequate level of AADT to use AIPV due to the low speed (5 to 15 mph) of usual AIPV operation. Since there is no clear guidance on this matter, State DOTs adopt their own criteria. Colorado DOT is using an AIPV on low-volume roads (AADT less than 6000). That may apply for different states like New York where most roads are much busier than that. Therefore, there is the need to develop clear specifications and guidelines regarding AIPV use in Pennsylvania. Testing Pennsylvania allows the testing of highly automated vehicles (HAV) under current state law provided that a licensed driver is behind the wheel, with the ability to intervene in the HAV operation if necessary [103]. AIPV allows for testing to be conducted with a driver in the follower truck but it is unclear how much controlling power the driver is allowed. There is the need to define for which operation would benefit most from using AIPVs. From initial testing in other states, recommendations are being made for slow moving maintenance and cleaning operations. Testing can be conducted initially in test tracks and then moved to low volume road operations. The pool study suggested potential areas of future research for AIPV broader implementation [95]: 67

1) Calibration frequency 2) Standard verification procedures 3) Standard Concept of Operations 4) Cybersecurity 5) Crash protocols

Training Extensive training of operators is needed as the AIPV system required the driver to make decisions, not only from the LT’s perspective, but also considering the potential implications on the FT operation [95]. Training protocols need to be established regarding the deployment decisions as discussed before. Costs and developers The leading developer of the leader-follower AIPV system is Kratos Defense & Security Solutions, Inc. a US National Security developer. The Pitt Gazatte (2018) estimated that the cost to truck plus system is $350,000 according to conversations with the Royal Truck & Equipment marketing department [59]. There is also the possibility to retrofit existing trucks with the system. Royal Truck & Equipment also offers leasing options. All of these come with technical support and onsite training.

4. Summary and Recommendations A remote-controlled tech scan brief analyzed over 20 technologies with promising safety benefits for pavement construction, inspection, and maintenance. Three of these technologies were selected for an in-depth review, namely, the Automated Real-Time Thermal Profiling for Asphalt Paving, the remote-controlled Density Profiling System (DPS), and the Autonomous Impact Protection Vehicle. A comprehensive performance and construction safety features of each technology were summarized in a detailed literature review. Finally, the technologies were classified based on their feasibility of implementation in Pennsylvania using the following criteria: • • •

Technologies ready for immediate implementation. Technologies requiring addition development, but able to be implemented in the near future. Technologies promising significant benefits but requiring substantial enhancement prior to implementation.

4.1. Automated Real-Time Thermal Profiling for Asphalt Paving The Automated Real-Time Thermal Profiling for Asphalt Paving system showed many benefits. Besides removing the need for inspectors to actively collect data directly from the paving work and thus reducing their exposure to hot materials and heavy equipment, the technology also has the potential to improve quality of asphalt paving by providing continuous, full coverage, and high accuracy temperature data for both operators and inspectors. In addition, the technology provides real-time paver speed which can also be used for quality assurance. The Paver Mounted Thermal Profiler (PMTP) system has been tested and approved by many state DOTs over the last 15 years with a great cost-benefit ratio. Therefore, this technology is classified as ready for immediate implementation. We recommend the following steps for the initial implementation of this technology: 1) Perform demonstrations of the device in real paving projects to show the benefits of the technology. 2) Conduct training of operators and inspectors to access and interpret the collected data. 3) Develop a strategic plan in case of real-time data showing cold asphalt mat areas. 69

4) Establish acceptable and unacceptable temperature and temperature variation criteria as measured by the PMTP. 5) Modify PA specifications to include the use of the PMTP. 4.2. Remote Controlled Density Profiling System The remote-controlled DSP (Figure 40a) technology is in the final stages of development. The robotic prototype has been successfully tested in different scenarios. However, the device has not yet been deployed in any major asphalt pavement project. Additional features are in development with focus on faster scanning, marking of areas of interest, inclusion of collision sensors, and GPS simplifications. This way, the DPS robot was classified as a technology promising significant benefits but requiring substantial enhancement prior to implementation. However, IRISE members can benefit from the use of the conventional man operated DPS (Figure 40b) before the robotic platform is commercially available. The conventional DPS provides abundant high-quality asphalt density information without the need to extract material (coring) from the constructed pavement. The conventional DPS is classified as ready for immediate implementation and its implementation will provide guidelines for the use of the robotic platform in the near future.

(a)

(b)

Figure 40: Density Profiling System (DPS) in a) robotic platform, and (b) conventional man operated cart. We recommend the following steps for the initial implementation of the conventional DPS: 1) Perform demonstrations with coring on marked locations to attest the device accuracy. 2) Conduct training of operators to calibrate the device according to the asphalt mixture being used. 3) Conduct training of operators and inspectors to interpret the collected data. 4) Develop time-effective data collection protocols. 5) Refine methodologies for use of DPS for determination of optimum roller patterns. 6) Develop strategic plans in case of low-compaction data. 7) Establish compaction criteria for the use of the DPS along with coring. 8) Modify PA specifications to include the use of the DPS.

4.3 Autonomous Impact Protection Vehicle The Autonomous Impact Protection Vehicle is a novel technology that is capturing the attention of many state DOTs because it removes the deliberate risks associated with driving an impact protection vehicle. The recent innovations in connected and automated vehicles permitted significant improvements in the technology and addressed many limitations identified in earlier field trials. Nevertheless, substantial testing and training might be required for implementation in PA. These observations led to the classification of the AIPV as a technology requiring addition development, but available for the implementation in the near future. We recommend the following steps for the initial implementation of the conventional DPS: 1) Perform demonstrations of the AIPV in low-volume roads to attest the system functionality and safety. 2) Establish various pavement maintenance activities and traffic volumes that would be optimal for AIPV. 71

3) Conduct training of operators (drivers) in various scenarios. 4) Conduct training of working crews for familiarization of workers with the AIPV and its emergency features. 5) Update current crashing protocols of conventional impact protection vehicles to include the AIPV. 6) Develop new PA specifications to include the use of the AIPV.

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83. Saarenketo, T. and P. Roimela. Ground penetrating radar technique in asphalt pavement density quality control. Proceedings of the seventh international conference on ground penetrating radar. Vol. 2., 1998. 84. Popik, M., K. Maser, and C. Holzschuher. Using high-speed ground penetrating radar for evaluation of asphalt density measurements. Annual conference & exhibition of the transportation association of Canada, Canada., 2010. 85. Hoegh, K., Khazanovich, L., Dai, S., and H.T. Yu. Evaluating asphalt concrete air void variation via GPR antenna array data. Case Studies in Non-destructive Testing and Evaluation: 2733, 2015 86. Tang, Q., Cheng, Y., Hu, X., Chen, C., Song, Y., Qin, R. Evaluation Methodology of Leader-Follower Autonomous Vehicle System for Work Zone Maintenance. Transportation Research Record. Vol 2675I5) 107 – 119, 2021. 87. Missouri Department of Transportation. Leader-Follower Truck Mounted Attenuator System Request for Proposal. Jefferson City, MO, 2018. 88. Theiss, L., Bligh, R. P. Worker Safety During Operations with Mobile Attenuators. FHWA/TX-13/0-6707-1. Texas Department of Transportation, 2013. 89. Wenall, J. Alternative TMA carriers: Crash test with a tractor, an articulated front-end loader and a rigid frame. VTI rapport 684A. VTI, Sweden, 2009. 90. Ross, H.E. Jr., D. L. Sicking, R.A. Zimmer, and J. D. Michie. Recommended Procedures for the Safety Performance Evaluation of Highway Features. NCHRP Report 350, National Cooperative Highway Research Program, Transportation Research Board of the National Research Council, Washington, D.C., 1993. 91. Buth, C. E, R. M. Olson, J. R. Morgan, W. L. Campise, and J. C. Heslop. Truck-Mounted Attenuators. Report No. RF 7015, Texas Transportation Institute, College Station, Texas, March 1986. 92. MNDOT. Governor’s Advisory Council on Connected & Automated Vehicles. Destination CAV, MNDOT 2020. 93. Blazina, E. Legislature pushes automated safety vehicles at PennDOT, turnpike maintenance sites. Pittsburgh Post-Gazette. 2018 94. PennDOT. REQUEST FOR INFORMATION FOR Highly Automated Work Zone Vehicles. RFI NUMBER 3518RFI02, Pennsylvania Department of Transportation, 2019. 95. Arrington, D., Tang, Q., Hu, X., Nylen, A., Weldon, T., Drum, T. Autonomous Maintenance Technology Literature Review. CDOT-2021-06. Colorado Department of Transportation, 2021. 96. Kohls, A. G. Autonomous Truck Mounted Attenuator (ATMA) Pilot. RES 2019-15. Tennessee Department of Transportation. 2020. 97. Transportation Research Board. Enhance Work Zone Safety with New Technologies. TRB Webinar presentation, 2020. 98. Descant, S. Colorado DOT Launches Autonomous Vehicles to Improve Worker Safety. 2017. Avaliable at: https://www.gov tech.com/fs/data/Colorado-DOT-Launches-Autonomous-Ve hicles-to-Improve-Worker-Safety.html. Accessed on October 12th, 2021 99. Colorado Department of Transportation. Autonomous Maintenance Technology (AMT) Pool Fund. 2018. http:// www.csits.colostate.edu/autonomous-maintenance-technolo gy.html. 78

100. Hu, X. and Tang, Q. (2020). Modeling and Development of Operation Guidelines for Leader- Follower Autonomous Truck-Mounted Attenuator Vehicles. Mid-America Transportation Center, University of Nebraska–Lincoln, Missouri University of Science and Technology. 101. PennDOT (2021). CAV Initiatives. Available at: https://www.penndot.gov/ProjectAndPrograms/ResearchandTesting/Autonomous%20_Vehicles/ Pages/CAV-Initiatives.aspx. Accessed on October 12th, 2021. 102. PennDOT. Temporary Traffic Control Guidelines. Publication 213. Pennsylvania Department of Transportation, 2021. 103. PennDOT (2021). CAV Initiatives. Available at: https://www.penndot.gov/ProjectAndPrograms/ResearchandTesting/Pages/AutonomousVehicle-Testing-FAQs.aspx. Accessed on October 12th, 2021.

Appendix A: IRISE survey The IRISE member’s survey was composed of two steps regarding areas in which a remote-controlled approach would be most welcomed and remote-controlled technologies that the members already knew or were familiar with. The survey prompted participants to respond to these questions: 1. Which areas/activities of pavement construction and inspection do you think would benefit from remote-controlled technologies? 2. Please list any remote-controlled technology for pavement construction or inspection that you know of or are interested in. Tables A.1 and A.2 present the survey results for questions 1 and 2, respectively. Responses were edited for length and clarity. Table 1.A – Survey responses for Question 1 # AREA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Asphalt pavement placement (IR data that identifies thermal segregation) Reinforcement bar tying technologies. Lidar (from drones) for final grading operations Possible lidar use for pavement smoothness evaluations Subgrade Sub-Base Pavement Layers Asphalt Paving Determining Pavement IRI - unmanned Analysis of existing roadway conditions Correct milling depth, cross slope, or profile, of asphalt roadways Bridge deck rebar replacement and rebar tying 3D Laser scanning for as built Augmented reality for construction Visual Observation Joint Inspection 80

17 18 19 20

Ticket Processing Preliminary Evaluation (for deeper patches, slab stabilization, etc.) Post Construction Evaluation Areas closer to live traffic, i.e., joint locations Table 1.A – Survey responses for Question 2

# TECHNOLOGY 1 Lidar for final cut / fill quantity determination 2 Cameras to monitor truck bed cleanliness at asphalt plants Camera system to inspect / measure concrete truck fins inside of the concrete 3 mixer. Camera system to automatically monitor blows per inch during pile driving 4 operations 5 Intelligent compaction 6 Total station control 7 Nano particle concrete additives 8 Real time IRI profiler 9 Dowel bar scanner 10 Computer/GPS driven Paving Technologies 11 Intelligent Compaction technologies 12 Lidar Roadway assessment 13 MIT Scan analysis - Concrete Pavements 14 Tybot 15 Topcon, Trimble, Leica for terrestrial scanning 16 Visual Live/ Unity 17 Drone 18 E-Ticketing 19 Ground Penetrating Radar

Appendix B: Technology Transfer Workshops The first workshop with focus on the Autonomous Impact Protection Vehicle (AIPV) was conducted virtually on April 6th, 2022, with a presentation from the major vendor and developer of this technology. Over forty participants attended the workshop. For the other technologies, a combined workshop was held in-person (with virtual participation as well) on May 24th, 2022, with presentations from experts in both technologies. Again, over forty participants attend the workshop. A field demonstration of the density profiling system (DPS) was conducted at the end of the IRISE Annual Meeting on May 25th, 2022, in a parking lot. Another field demonstration of the DPS was conducted for Allegheny County and invitees on June 9th, 2022, in an active paving project. Recordings of these presentations are available at the IRISE website.

B.2. Autonomous Impact Protection Vehicle Workshop The workshop was presented by Maynard Factor from Kratos Defense and Security Solutions. Mr. Factor is the Vice President of Business Development for the Kratos Defense & Security Solutions Unmanned Systems Division. He is responsible for the generation of new business opportunities, world-wide sales, and marketing, and provides technical direction for long range strategic planning including the development of new products, capabilities, and internal research and development endeavors. Kratos Defense and Security Solutions works in collaboration with Royal Truck and Equipment Inc. to develop the autonomous truck mounted attenuators (ATMA) also known as Autonomous Impact Protection Vehicle (AIPV).

B.2.1. ATMA workshop presentation minutes Standard truck mounted attenuators (TMA) are regularly used in moving work zones in all 50 states and worldwide. Moving work zones are common practice for operations like line painting or patching. These work zones typically move at speeds of 7 – 15 mph along regular

moving traffic. TMA are designed to be mobile crash barriers that prevent distracted drivers from entering work zones potentially injuring or killing workers. Currently, TMA must be driven manually which is a very dangerous assignment. If the TMA is struck, it prevents injuries to workers inside the construction zone but can cause serious injury or death to the TMA driver. Automation would move the worker away from the danger zone and allow them to continue to operate the TMA. The goal is not to eliminate this job but to convert them from a driver in a dangerous position to an operator from afar. To permit driverless vehicles, Pennsylvania passed ACT 117 which allows driverless construction vehicles for maintenance. This was spurred by TMA driver testimonials which encouraged the development of safer construction practices. The automatic truck mounted truck attenuators (ATMA) designed by Kratos Defense and Security Solutions and the Royal Truck and Equipment Inc. were created for this purpose. ATMA use a multi-vehicle configuration which Kratos call “leader/follower”. The vehicle performing the work zone operation is the leader and the ATMA follows driverless. A retrofit kit can convert any existing fleet vehicle into an autonomous vehicle to use in an ATMA system. The kit attached to the leader vehicle will collect navigation data to send to the ATMA. The kit attached to the ATMA will install the driverless technology. The operator, now located in the leader vehicle, can adjust the ATMA vehicle alignment and follow distance in real time. The ATMA can follow within an accuracy of 1 inch in a straight path and 3 inches around turns. ATMA has obstacle detection on all sides and will automatically stop if sensors are tripped. There are redundancies built in for communication and navigational links to prevent connection failure. There are also security protocols that prevent outside interference and hacking. Separate stop buttons are located in the leader vehicle and outside the following ATMA vehicle for emergencies. Retrofit kits are adaptable but they ask for an automatic transmission for ease of use and a newer model vehicle which will not be replaced soon. ATMA systems have been deployed in 8 states and England through either purchase or lease. At this time, not all states allow for driverless construction vehicles and therefore require a driver in the ATMA. This allows workers and communities to familiarize themselves with the

technology until legislation is passed. ATMA can easily transition from automated to manual if the need arises. This system has had many positive reviews and constructive comments which have been actively used to farther improve the technology. Several awards have been given for this system as well. ATMA have clear benefits including worker safety and a simple introduction to autonomous vehicles for departments of transportation.

B.2.2. ATMA workshop Q&A Some questions and concerns were raised by the attendees. The main questions concerned pricing and deployment requirements. To purchase an ATMA system, the initial cost would be about $350,000. This would include training, installation, and deployment assistance as well as the two full kits: one for the lead vehicle and one for the follow vehicle. After comfort is established with the system, additional systems would cost about $150,000 - $200,000. However, if the vehicle to retrofit is different than those currently used, additional cost is required to design the retrofit kit to the new vehicle. Leasing is also an option either through pre-configured vehicles or through temporary retrofit installation. The first week is at least $20,000 depending on transportation or installation of equipment and training. Additional time would cost about $10,000 per week. To deploy, it typically takes 6 months to a year depending on many factors including location and previous experience in automation. There was a question regarding the top speed of the driverless vehicle. An ATMA has a standardized speed limit of 20 mph, but it is adjustable up to 70 mph. There was a concern on the effect of hills and slopes on navigation to which this issue was previously discovered and addressed so that the latest ATMA technology does not have this problem. Another concern was if a passenger vehicle accidently got between the leader and follower vehicle. An interrupting vehicle does not disrupt the connection unless the vehicle is within the obstacle detection range. The ATMA would then stop automatically. If the leader instructs the ATMA to continue operation, the ATMA would continue following the navigation path set by the leader at a slightly elevated speed until the set follow distance is recovered. 84

A final note was the existence of a pooled fund of information on deploying ATMA shared between 13 states. This includes experiences, user manuals, and training procedures. Currently, Pennsylvania is not part of this fund.

B.3. Automated Real-Time Thermal Profiling & Density Profiling System Workshop This workshop had four presenters. First, Joe Reiter from Applied Research Associates (ARA) talked about the Paver Mounted Thermal Profiler. Mr. Reiter works as a senior engineer at ARA specializing in non-destructive testing of pavement systems, materials, and construction. He participated in the SHRP2 R06C study [52] using infrared scanning of new construction asphalt and presented demonstrations of the paver mounted thermal profiler (PMTP) from 2015 to 2019. In the Density Profiling System (DPS) part of the workshop, Dr. Lev Khazanovich, Anthony Gill Professor at the University of Pittsburgh, introduced Ground Penetrating Radar (GPR) concepts and the DPS technology. Dr. Khazanovich has many years of experience with GPR and DPS for asphalt inspection. He participated in the SHRP2 study [63] which focused heavily on field testing and procedures for DPS. He was followed by Dr. Kyle Hoegh from the Minnesota Department of Transportation talking about DPS benefits and implementation. Dr. Hoegh currently leads a pool-funded study with several institutions to implement DPS. Finally, Ken Corcoran from GSSI presented the robotic platform for the DPS technology. Mr. Corcoran is a GPR product specialist with 25 years of experience with GPR.

B.3.1. Automated Real-Time Thermal Profiling & Density Profiling System workshop presentation minutes The first presentation was on Paver Mounted Thermal Profilers given by Joe Reiter from ARA. He began by discussing the importance of temperature monitoring as it is an indicator of density non-uniformity. If temperature differentials are not known during construction, the roller pattern cannot be altered to the new temperature to correct the density. Traditional temperature measurements rely on point-based methods like standard temperature guns or infrared cameras. 85

These methods can identify temperature differences but only at certain chosen points and relocating problem areas can be challenging. The methods also require the inspector to stand behind the paver which can be dangerous, especially at night. To remove inspectors from hazard areas, a continuous temperature infrared scan was developed that is attached to the back of the paver. This provided an improved temperature profile but was a safety concern for workers on the back of a paver. The paver mounted thermal profiler is a single scanner mounted on the paver, above the workers, which pivots to scan the pavement width. This method creates a thermal profile across the entire pavement as it is being paved providing more data with greater accuracy. The thermal profiler has a display inside the paver which shows the operator the average temperature across the width, the full thermal profile, paver speed, an analysis on uniformity, and more. The data allows contractors to adjust construction practices in real time. It also removes temperature data from any obstructions, only considering the pavement. Data gathered by this device can be stored on a USB or the cloud. The cloud will update automatically and constantly, allowing inspectors and others to observe the paving from anywhere. This device is primarily used as a quality assurance tool. Contractors can use it to monitor production and placement, minimize penalties, and as a forensic tool. Agencies can also benefit by identifying areas to sample, determine why a pavement fails in the future, and reduce or prepare for future distresses and maintenance. Overall, it helps to determine what and when changes are needed during paving by using temperature variation to predict density variation and reconsider roller patterns to correct the issue. Lessons learned in this research were thermal profilers reinforces good construction practices, provides information to use to adjust roller patterns to get uniform density, and provides information on when to add delivery trucks or tarps. The second presentation on Density Profiling Systems (DPS) was given by Dr. Lev Khazanovich from the University of Pittsburgh. He began by stressing the importance of good density to improve rutting and fatigue cracking resistance and to reduce moisture penetration. Density is traditionally measured by coring or nuclear density gauges, but both these methods have well-known issues. A nondestructive test called ground penetrating radar (GPR), which 86

measures the amplitude of a reflected signal which depends on the dielectric constant of the pavement, presents many benefits. The dielectric constant is the material property to store electric energy. A low dielectric constant means there is a high air void content resulting in lower density. The DPS is a high frequency, air coupled device that measures the dielectric constant providing real time density estimations. This allows users to continuously monitor compaction uniformity. Small changes in the dielectric constant causes a large change in air content therefore high accuracy is necessary. The device must also only consider the newly paved layer, not the underlying structure. DPS is a simple device that requires minimal training. The user can immediately follow the paver to quickly acquire and analyze the dielectric data. This device is also geotagged so areas of interest can easily be revisited. DPS is not able to directly measure air voids, measure thickness, or consider underlying layers or subsurface objects. Many of these can be done using other GPR types but not DPS. DPS requires calibration to determine the air void content from the dielectric constant and the calibration depends on each individual mixture. Other devices such as intelligent compaction or infrared sensors can be used in tandem to analyze different aspects. The next presentation was on moving Toward Improved Asphalt Pavement Density Acceptance given by Dr. Kyle Hoegh with the Minnesota Department of Transportation. This effort was part of a collaboration of 14 agencies and the Federal Highway Administration. Density profiling systems (DPS) provide real time continuous density assessment of the placed asphalt after the finish roller and the goal of this project is the use DPS to improve pavement density and construction practices. DPS would allow improved coverage and comprehensiveness of density measurements and ultimately reduce coring. Pavement density is highly dependent on construction practices and coring does not provide enough information to locate low density areas. Density mapping can be used as a process control tool to adjust field work and certain aspects are needed for accurate mapping. Dr. Kyle showed several two examples of this adjustments done in real-time during paving projects. The correct equipment must be used that 87

provides good precision and standardized sensors. Sensor standardization includes regular sensor testing using a line test or a steel of fiberglass material scan to test for sensor consistency. Improved calibration can be performed on gyratory compacted asphalt samples to remove coring from calibration. This method falls well within the repeatability requirements and provides stable results. They still recommend coring in the field for data validation. A routine collection habit is needed that requires the same number of passes and user movement method (walk verses driving). Proper training must also be a requirement. The final presentation was on the Pavescan Robotic Platform given by Ken Corcorank with GSSI. He was able to receive user feedback on density mapping devices that was both technical feedback and user experience. It was noted that there was still a safety concern with nearby traffic and with technicians walking many miles on the hot mat. There were three new methods to the traditional pushed device: segways for technicians, vehicle mounted device, or remote-controlled robots. This presentation focused on the robotic platform. The first model had the sensors attached externally to the robot. The robot itself was light with a long battery life that was able to last the workday. The remote-controlled aspect removed the technician from hazardous areas or a line mode setting automatically drives the robot in a grid formation. The second model improved the device by internalizing the sensor and connecting the sensor to the robot battery. A grid mode setting was designed in an app to determine scan distances and path types the robot can automatically drive. The remote-controlled option is also still available. The robot creates a dielectric constant contour map within the app. The next steps for the robotic device are to scan faster, mark areas of low dielectric constant with chalk on the pavement surface, add collision sensors, develop smarter route planning, and to simplify the GPS. On May 25th a DPS device was demonstrated in a parking lot at the University of Pittsburgh campus. Attendees of the demonstration were able to calibrate and operate the device (Figure B.1a) measuring dielectric constant and levels of compaction of the existing asphalt layer. A week later the device was demonstrated for Allegheny County and invitees in an active paving project (Figure B.1b).

(a)

(b)

Figure B.1 – DPS field demonstration at (a) the IRISE annual meeting, and at (b) an active paving project. B.3.2. Automated Real-Time Thermal Profiling & Density Profiling System Workshop Q&A For the automated real-time thermal profiling presentation, the audience had several questions for the presenter. One was concerning how the workers know where the scan is occurring and how to stay out of the way. They simply inform the workers of its locations and advise to avoid when possible. Another question was how much the device costs to buy or rent. It was advised to contact the manufacturer for renting costs but buying costs range between $35,000 and $45,000 depending on installation. There is also an annual fee to access the cloud. There was a concern about the security of the cloud. It is password protected but for additional information, contact the manufacturer. An audience member asked what the biggest issue is in implementation. The presenter said that they are having difficulties explaining that it is supposed to be used to improve the paving process. It is not a device to highlight errors in a negative way, but to show where improvements can be made. Finally, there was a question on using the device with incentives and disincentives. The Minnesota Department of Transportation requires this device on all their pavers and has incentives and disincentives based on temperature

differentials which has been used with success. Federal funds cannot use incentives because they do not have independent measurements. On the DPS presentations, there was a question on the effect on surface water on DPS measurements. It is a minimal concern since the device is used immediately after paving when there will not be surface water. There was another question on the use on concrete, but DPS is not for concrete use. Finally, there was a question on a roller mounted DPS which are currently being researched.

Swanson School of Engineering Department of Civil and Environmental Engineering IRISE Consortium 742 Benedum Hall 3700 O’Hara Street Pittsburgh, PA 15261

The information printed in this document was accurate to the best of our knowledge at the time of printing and is subject to change at any time at the University’s sole discretion. The University of Pittsburgh is an affirmative action, equal opportunity institution.

REMOTE-CONTROLLED TECHNOLOGY ASSESSMENT FOR SAFER CONSTRUCTION

Articles inside

Appendix B: Technology Transfer Workshops

Appendix A: IRISE survey

References

operated cart

Figure 38: AIPV system layout [97

accuracy tests: (a) following accuracy, (b)lane changing, (c) roundabout operation, (e) minimum turn radius, (f) U-turn [86

Figure 35: Impact testing of TMA on a tractor [89

Figure 37: AIPV system overview [95

Figure 36: Accident involving IPV of the Virginia DOT [92

Figure 33: Dielectric Maps from Joint Surveys of I-95 near Pittsfield, Maine [63

Figure 32: Joint survey [63

Figure 27: A prototype of MnDOT remotely operated rolling asphalt density meter

Figure 30: Real-time data visualization and comparison with cores [63

Figure 31: Cherryfield, Maine calibration model [63

Figure 24: Cleaned temperature profile [52

Figure 23: Examples of Pave Project ManagerTM detailed reports with temperature profiles and paver speed or time diagram [53

Figure 25: PDP instrument background principle of operation [73

Table 3: Specification recommendations for LaDOTD [48

Figure 22: On-board computer output for real time feedback [53

Figure 19: Temperature segregation identified with thermal imaging [47

Figure 6: Conduit remote inspection using (a) crawler robot (b) UAS [22

Figure 5: Marker placement with (a) manual method and (b) automated system [19

Figure 21: Infrared sensors attached to paver for real-time thermal data acquisition [52,53

Figure 20: Distress due to temperature segregation causing inadequate compaction [50

Figure 9: Infrared sensors attached to paver for real-time thermal data acquisition [26] and the latest version of IR temperature scanners [27

Figure 18: Autonomous impact protection vehicle [44

Figure 4: Example of bridge deck demolition using a remote-controlled robot [15