GTI - Project Spotlight May 2021

Tuesday May 18th, the Georgia Transportation Institute hosted a 1-hour webinar that spotlighted three recently completed GDOT Research projects. Each project had maximum of a 15-minute presentation, with a short time afterward for questions. If you miss the Spotlight, you can watch it on the YouTube Link.

YouTube Webinar Link

Presenter: Jay Kwon, Ph.D., P.E. Assistant Professor of Civil Engineering | Kennesaw State University - Department of Civil and Environmental Engineering

Dr. Jay Kwon specializes in geotechnical engineering with research interests and expertise in characterization and sustainable use of geomaterials, structural pavement performance monitoring using the innovative non-destructive testing device, and dynamic response measurement and analyses of pavement and railroad track systems. Dr. Kwon has 21 years of research and industry experience in the field of transportation geotechnical engineering. He has authored and co-authored over 50 peer-reviewed publications and conference papers from his research projects with a corresponding h-Index of 17 and 875 citations. And is an active member of the Transportation Research Board (TRB) and serves as handling editor of the Transportation Research Record: Journal of the Transportation Research Board. Dr. Kwon is a member of TRB technical committees on Aggregates (AKM80) and Geosynthetics (AFG80). Dr. Kwon is a member of the American Society of Civil Engineers (ASCE) Transportation & Development Institute (T&DI) and Geo-Institute and served in the International Activities Council.

Title: GDOT RP18-22: Full Depth Pavement Reclamation: Performance Assessment and Recommendations for Best Performance

Abstract:

This study was to aim to identify factors that affect the performance of the Full Depth Reclamation (FDR) pavement. Current practice in FDR construction was assessed, with emphasis on the use of deflection testing in Quality Control. The deflection tests results permit an evaluation of the effects of variability in measured properties on the performance of reconstructed pavement with FDR. A series of mechanistic analysis was performed using the FDR base thickness and its compressive strength obtained from field and laboratory tests to evaluate the impact of the strength and thickness variability of the FDR layer on the pavement performance. The mechanistic sensitivity analysis results indicated that the thickness and modulus of the FDR base layer have a significant influence on the fatigue cracking performance of the FDR. The predicted pavement performance show that the service life of the FDR pavement section could decrease by three times if design strength and thickness requirements have not been achieved.
The findings of this study could be used to refine and update standards and specifications for FDR. Results from the mechanistic analysis will improve the structural design process of FDR by considering variability in material properties. The research results demonstrated the influence of FDR base thickness and strength on the pavement performance. To reduce variability in FDR strength, it is recommended that GDOT specifications be modified for stricter control on the cement spread rate and treatment depth.

Presenter: Stephan Durham, Ph.D., P.E., Assistant Dean for Student Success and Outreach and Professor of Civil Engineering | The University of Georgia - College of Engineering

Dr. Stephan Durham led the development of the new Civil Engineering degree program through its first full accreditation in the College of Engineering at the University of Georgia in 2015. Currently, he serves as the Assistant Dean for Student Success and Outreach of the College of Engineering and Professor in Civil Engineering. He came to UGA from the University of Colorado Denver (UCD) in January 2012 where he was an Associate Professor in the Department of Civil Engineering. Since joining academia in 2005, Dr. Durham graduated over 30 MS and Ph.D. students in the area of structural engineering, concrete materials, sustainability, and construction management. He has performed past studies for Georgia Department of Transportation, Colorado Department of Transportation, Federal Highway Administration, Environmental Protection Agency, Colorado Department of Public Health and Environment and private industry. He has received numerous awards that include selection as the 2020 UGA Engaged Scholar Award, 2018 UGA College of Engineering Faculty Fellow, 2016 ASCE Civil Engineer of the Year Award, 2016 Outstanding Instruction Award, 2015 Lowry Gillespie, Jr. Curriculum Enhancement Award, 2014 American Concrete Institute Young Member Award for Professional Achievement, the 2013 Georgia Engineer of the Year in Education Award, 2012 University of Arkansas Outstanding Young Alumnus Award, and the Walter P. Moore Jr. Faculty Achievement Award. He teaches undergraduate and graduate courses in structural materials, construction management, reinforced and prestressed concrete design, and advanced concrete materials. Much of his instructional collaborations are with the UGA’s Archway Partnership, J.W. Fanning Institute for Leadership Development, Carl Vinson Institute of Government, and the UGA Marine Extension and Georgia Sea Grant. His research interests include innovative concrete materials and mixtures, design and evaluation of pavements, non-destructive testing, and evaluation of construction practices. He is an active member in the American Society of Civil Engineering where he serves as legislative contact and advocacy captain for the State of Georgia and recently appointed to the society’s Transportation Policy Committee. He obtained his BSCE, MSCE, and Ph.D. in civil engineering with an emphasis in structural engineering from the University of Arkansas. He is a licensed Professional Engineering in the state of Georgia.

Title: GDOT RP 17-09: Phase II – Investigation of Recycled Tire Chips and Fiber Reinforcement for Use in GDOT Concrete Used to Construct Barrier Walls and Other Applications

Abstract:

Concrete median barriers (CMBs) are installed to decrease the overall severity of traffic accidents by producing higher vehicle decelerations. In 2016, an update to the AASHTO Manual for Assessing Safety Hardware (MASH) saw a 58.00percent increase in impact severity of test level 4 (TL-4) impact conditions when compared to the NCHRP Report 350 testing criteria. This study investigates the use of fiber-reinforced rubberized CMBs in dissipating the impact energy to improve driver safety involved in crashed vehicles. This study was completed in three major investigations: (1) fiber-reinforced rubberized concrete mixtures evaluation, (2) finite element model (FEM) and laboratory-scale barrier wall testing and simulations, and (3) steel fiber–reinforced concrete (SFRC) mixture design and testing. The fiber-reinforced rubberized concrete mixture investigation examined the energy dissipation capacity of fiber-reinforced rubberized concrete mixtures subjected to impact forces. Results from this testing confirmed that fiber-reinforced rubberized concrete demonstrated significantly improved energy dissipation capacity and impact resilience, particularly with 1.00 percent steel fiber addition and 20.00 percent tire chips. An FEM was developed in order to perform a vehicle crash simulation of a single-slope CMB as a viable alternative to a full-scale crash test. Full-scale CMB prototypes incorporating shear keys were tested by applying TL-4 quasi-dynamic impact conditions. An additional investigation was performed to evaluate the influence of steel fiber volume and geometry on fresh and hardened concrete properties as well as the influence on the flexural and shear capacities of scaled laboratory beams. Lastly, machine learning methods were used to construct SFRC compressive and flexural strength prediction models.

Presenter: Yi-Chang “James” Tsai, Ph.D., Professor of Civil Engineering | The Georgia Institute of Technology - School of Civil and Environmental Engineering

Dr. James Tsai is a professor in the School of Civil and Environmental Engineering at Georgia Tech, and he is an adjunct professor in the School of Electrical and Computer Engineering. Dr. Tsai’s research focuses on 1) applications of advanced sensor technologies (2D imaging, 3D lasers, 3D LiDAR, UAV, and smart phones, CV/AV), machine learning, and computer vision to automatic infrastructure health and safety condition assessment and monitoring, 2) optimized pavement asset management, and 3) roadway safety analytics. Dr. Tsai had also led more than $3.5M of competitively selected research projects on “Remote Sensing and GIS-enabled Asset Management (RS-GAMS),” sponsored by the USDOT Office of the Assistant Secretary for Research and Technology (USDOT/OST-R), from 2010 to 2014. These projects were designed to intelligently assess roadway asset health conditions by using emerging sensor technologies installed in the intelligent Georgia Tech Sensing Vehicle (GTSV), along with artificial intelligence and machine learning. These were the first research projects sponsored by USDOT to evaluate the use of 3D laser technology for automatic detection and classification of pavement distresses. Dr. Tsai is a leader and pioneer on applying 3D laser technology for automatic detection and classification of pavement distresses (cracking, rutting, raveling, faulting, potholes, etc.); 3D laser technology has replaced 2D technology and has currently become the mainstream technology in the US for automatic pavement distress detection.
Dr. Tsai has served on the technical committee of the United States National Cooperative Highway Research Program (NCHRP 20-102 (06) “Road Markings for Machine Vision” and the NCHRP 20-102(28) "Preparing Transportation Agencies for Connected and Automated Vehicles in Work Zones"), two of 34 connected and automated vehicle research projects sponsored by the USDOT. Dr. Tsai gave a talk on “Dynamic Mapping of Roadway Health and Safety Conditions Using Artificial Intelligence” at the Connected Fleets USA 2019 on November 13, 2019. From 2008 to 2015, Dr. Tsai served on the Expert Task Group (ETG) of the US National Strategic Highway Research Program II (SHRP II) for the Naturalistic Driving Study (NDS) to provide guidance on research focuses, including the use of computer vision for processing and analyzing big NDS data. He is currently serving on the technical committee of the AFD 10 Pavement Management Systems of the Transportation Research Board in the National Academies. Since 2010, he has served as the Associate Editor of ASCE Journal of Computing in Civil Engineering.

Title: GDOT RP 17-32: Validating Change of Sign and Pavement Conditions and Evaluating Sign Retro-reflectivity Condition Assessment on Georgia’s Interstate Highways Using 3D Sensing Technology

Abstract:

This project is focused on two main objectives a) assessing and analyzing the change of the traffic sign retro-reflectivity condition, and b) analyzing the change of pavement surface distress, using the 3D sensing data collected on Georgia Interstates in 2015 and 2018. LiDAR and 3D Line Laser imaging technologies were integrated in the Georgia Tech Sensing Vehicle (GTSV) to collect the traffic sign and pavement distress data simultaneously. For the first objective, we developed a categorical traffic sign retro-reflectivity condition assessment method using mobile LiDAR, and we conducted a pilot study to demonstrate the feasibility of the newly developed method. This newly developed method can complement commonly used nighttime retro-reflectivity sign condition assessment methods and could potentially save approximately 60% of sign inspection efforts at the network-level by focusing on signs that need the most attention (poor or uncertain category signs), and quickly and efficiently identify poor retro-reflectivity condition signs for timely replacement. For the second objective, we updated the asphalt pavement condition of interstate highways using newly collected 3D pavement laser data and analyzed the change in asphalt pavement condition. In addition, we also collected the condition data of jointed plain concrete pavement (JPCP) and international roughness index (IRI) on interstate highways in this project using the 3D pavement laser data. 3D Line Laser imaging technologies have demonstrated to be consistent, reliable, and cost-effective and the outcomes from this project strengthens GDOT’s decision to adapt 3D laser technology for its automatic pavement condition evaluation to support maintenance activities, and optimally allocate pavement funds.