Dr. Zhongyu Yang is a Research Engineer II in the Construction and Infrastructure Systems Engineering group within the School of Civil and Environmental Engineering at the Georgia Institute of Technology, working with Dr. Yi-Chang (James) Tsai. He earned his Ph.D. in Civil and Environmental Engineering from Georgia Tech in 2024, his M.S. in Civil and Environmental Engineering from Georgia Tech in 2018, his M.S. in Civil Engineering from the University of Hong Kong in 2017, and his B.Eng. in Civil Engineering from Hunan University in 2016.
Dr. Yang’s research advances data analytics, machine learning, computer vision, and 3D laser technology to enhance the safety and sustainability of pavement and roadway infrastructure. Since 2017, he has contributed to multiple research projects funded by the Georgia Department of Transportation (GDOT) and the National Cooperative Highway Research Program (NCHRP), two of which were recognized as “High Value Research Projects” by the AASHTO Research Advisory Committee.
Dr. Yang’s research focuses on transportation safety and infrastructure asset management, with an emphasis on pavement systems. He integrates Geographic Information Systems (GIS), 2D and 3D pavement imaging and sensing technologies, data analytics, computer vision, and machine learning to support data-driven decision-making for state and local transportation agencies. His current interests include multi-temporal pavement crack propagation analysis for predictive and precision maintenance, AI-powered curve safety assessment using mobile devices, automated intersection crash diagram analysis, and the integration of crash risk indicators into pavement treatment prioritization.
| Ph.D. in Civil and Environmental Engineering | Georgia Institute of Technology | 2024 |
| M.S. in Civil and Environmental Engineering | Georgia Institute of Technology | 2018 |
| M.S. in Civil Engineering (Structural) | The University of Hong Kong | 2017 |
| B.Eng. in Civil Engineering | Hunan University | 2016 |
Dr. Yang’s teaching interests center on data analytics, geographic information systems (with an emphasis on ArcGIS), and the application of machine learning and 3D sensing technologies in civil and transportation engineering. He emphasizes hands-on, problem-driven learning that connects classroom concepts to real-world practice through authentic transportation and infrastructure datasets, including data shared by state DOTs. As Head Teaching Assistant for Georgia Tech’s Vertically Integrated Projects (VIP) program since 2019, and as Head Teaching Assistant and Instructor for CEE 4803 Data Analytics in Civil and Environmental Engineering, he mentors undergraduate and graduate students on open-ended projects in pavement management, roadway safety, and infrastructure asset monitoring, preparing them to translate emerging sensing, AI, and data analytics capabilities into practical engineering solutions.
2022: Zeitlin Innovation Award, Georgia Tech Entrepreneurial Impact Competition. Awarded to the SECurE (Smart Equipment Curve Evaluation) team for developing a low-cost smartphone-based solution for road curve safety data collection.
2021: AASHTO High-Value Research Project Award (Construction, Specifications, and Materials category), for GDOT RP 14-06: “Enhanced GDOT Pavement Preservation Guide with Optimal Timing of Pavement Preservation.”
2020: AASHTO High-Value Research Project Award (featured in AASHTO Research Supplemental Brochures), for GDOT RP 17-32: “Validating Change of Sign and Pavement Conditions and Evaluating Sign Retroreflectivity on Georgia’s Interstates Using 3D Sensing Technologies.”
1. Yang, Z., Fung, J. T. C., Ho, H., & Tsai, Y. C. (2025). Predictive and Precision Pavement Maintenance Methodology Utilizing Multi-Temporal Pavement Images. Transportation Research Record.
2. Yang, Z., Mohammadi, M., Wang, H., & Tsai, Y. C. (2024). A feature-based pavement image registration method for precise pavement deterioration monitoring. Computer-Aided Civil and Infrastructure Engineering.
3. Yang, Z., Yu, P., Shah, R., Knezevich, R., & Tsai, Y. C. (2024). Crash Prediction on Horizontal Curves: Review and Model Performance Comparison. Transportation Research Record.
4. Zhang, X., Hsieh, Y. A., Yu, P., Yang, Z., & Tsai, Y. J. (2023). Multiclass Transportation Safety Hardware Asset Detection and Segmentation Based on Mask-RCNN with RoI Attention and IoMA-Merging. Journal of Computing in Civil Engineering, 37(5), 04023024.
5. Hsieh, Y. A., Clark, S., Yang, Z., & Tsai, Y. J. (2023). Automated Concrete Pavement Slab Joint Detection Using Deep Learning and 3D Pavement Surface Images. International Journal of Pavement Research and Technology.
6. Mers, M., Yang, Z., Hsieh, Y. A., & Tsai, Y. (2023). Recurrent neural networks for pavement performance forecasting: review and model performance comparison. Transportation Research Record, 2677(1), 610-624.
7. Yang, Z., Zhang, X., Tsai, Y., & Wang, Z. (2021). Quantitative assessments of crack sealing benefits by 3D laser technology. Transportation Research Record, 2675(12), 103-116.
8. Bukharin, A. W., Yang, Z., & Tsai, Y. (2021). Five-year project-level statewide pavement performance forecasting using a two-stage machine learning approach based on long short-term memory. Transportation Research Record, 2675(11), 280-290.
9. Hsieh, Y. A., Yang, Z., & Tsai, Y. C. (2021). Convolutional neural network for automated classification of jointed plain concrete pavement conditions. Computer-Aided Civil and Infrastructure Engineering, 36(11), 1382-1397.
10. Tsai, Y., & Yang, Z. (2020). New pavement performance indicators using crack fundamental elements and 3D pavement surface data with multiple-timestamp registration for crack deterioration analysis and optimal treatment determination. Transportation Research Record, 2674(7), 115-126.