Olsson Hall 114B
151 Engineers Way
Charlottesville, VA 22904
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Professor Harris is chair of the Department of Civil and Environmental Engineering at the University of Virginia. His research and teaching interests focus on large scale infrastructure systems with a primary focus on condition monitoring and system performance. His research leverages image-based measurement techniques, simulation, visualization, and data analytics with applications in the areas of structural health monitoring, smart cities, and digital twins. Additional areas of focus include reinforced and prestressed concrete behavior, applications of innovative materials in civil infrastructure, and non-destructive evaluation. Dr. Harris’ research approach often utilizes a combination of laboratory and/or field investigations coupled with simulations.

Dr. Harris is an active member of in the American Concrete Institute (ACI), the Transportation Research Board (TRB), International Digital Image Correlation Society (IDICS), and the American Society of Engineering Education. He is also the former Director of the Center for Transportation Studies (CTS), as well as the former Faculty Director of the UVA Clark Scholars Program.


Ph.D., Civil Engineering - Virginia Tech, 2007

M.S., Civil Engineering - Virginia Tech, 2004​

B.S., Civil Engineering - University of Florida, 1999

"My research has direct real-world applications as my group strives to develop novel approaches to evaluate the performance of the built environment."

Devin K. Harris, Ph.D. Professor and Chair of the Department of Civil and Environmental Engineering

Research Interests

Structures and Mechanics - Sustainable Infrastructure Systems
Infrastructure Condition Assessment
Structural Health Monitoring
Smart Cities
Digital Twins
Applied Machine Learning

Selected Publications

"Robust Pixel-Level Crack Detection Using Deep Fully Convolutional Neural Networks " Journal of Computing in Civil Engineering, 2019, 33(6), 04019040 Alipour, M., Harris, D.K., and Miller G.R.
“Subsurface Damage Detection and Structural Health Monitoring Using Digital Image Correlation and Topology Optimization” Engineering Structures, 230, 111712. Dizaji, M.S., Alipour M., and Harris D.K.
“Failure Characteristics and Ultimate Load-Carrying Capacity of Redundant Composite Steel Girder Bridges: Case Study.” Journal of Bridge Engineering, 2015, 20(3), 05014012. Gheitasi, A. and Harris, D.K.
“Evaluation of Commercially Available Remote Sensors for Highway Bridge Condition Assessment”. ASCE Journal of Bridge Engineering – Special Issues: Nondestructive Evaluation and Testing for Bridge Inspection and Evaluation, 2012, 17(6), 886-895. Vaghefi, K., Oats, R., Harris, D.K., Ahlborn, T. M., Brooks, C. N., Endsley, K. A., Roussi, C., Shuchman, R., Burns, J. W., Dobson, R.

Courses Taught

CE 3300 - Structural Mechanics
CE 3330 - Introduction to Design of Structural Systems
CE 3700 - Properties and Behavior of Materials
CE 6310 - Prestressed Concrete Design
CE 6500 - Introduction to Bridge Engineering and Design


Delmar L. Bloem Distinguished Service Award 2021
IAspire Leadership Academy Fellow 2020–2022
Outstanding Reviewer - American Society of Civil Engineering - Journal of Bridge Engineering 2013
Excellence in Diversity Fellowship - University of Virginia Teaching Resource Center 2012–2013
ACI Young Member Award for Professional Achievement - American Concrete Institute 2011

Featured Grants & Projects

EAGER: Adaptive Digital Twinning: An Immersive Visualization Framework for Structural Cyber-Physical Systems (Award # 2136724) Role: Principle Investigator Applied to the domain of large-scale structural systems, this project will test the hypothesis that immersive engagement using a digital twin representation of these structural systems will enable participants to observe, interact, and contextualize the complex behavior mechanisms associated with these systems in their operational environment. To test the hypothesis, the research design will explore a series of technology innovations including the formulation of artificial intelligence models to emulate both simulation-based results and experiment-based measurements.
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NCHRP 23-16: Implementing and Leveraging Machine Learning at State Departments of Transportation The objective of this research is to advance the understanding and use of ML tools and techniques at state DOTs and other transportation agencies. The proposed research will aid state DOTs in transitioning to a more advanced state of practice.
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Performance Characteristics of In-Service Bridge for Enhancing Load Ratings Leveraging Refined Analysis Methods Role: Principle Investigator Year: 2018-2020 Sponsor: Virginia Transportation Research Council In this investigation, our team is focused on the exploration refined methods of structural analysis to aid in the evaluation of Virginia Bridges subjected to new federal requirements with respect to load rating for SHVs and Emergency Vehicles.
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