|408-Smart Cities: Intelligent Transportation Infrastructure Health and Safety Condition Assessment and Management Using Sensing and AI Technologies|
主讲人：Dr. James Tsai
时间：2019年5月27日（周一）下午 13:30 ~ 15:00
Dr. James Tsai is a professor of School of Civil and Environmental Engineering at Georgia Tech; he is also an adjunct professor of School of Electrical and Computer Engineering at Georgia Tech. Dr. Tsai has received his Ph.D. and MS degrees from Georgia Tech in 1994 and 1996 respectively, and received his BS degree from National Chung Hsing University, Taiwan in 1987. Dr. Tsai’s research focuses on 1) Smart Cities: Intelligent Transportation Infrastructure Asset Health Condition Assessment, Preservation, and Management, 2) New Transportation Ecosystems with Telematics and Connected/Automated Vehicles, 3) Pavement Technology, 4) Roadway Safety Analysis and Management, 5) Port/Freight Logistics, and 6) Applications of emerging sensing technologies, including 2D imaging, 3D lasers, 3D LiDAR, UAV, and smart phones, and GPS/GIS technologies with artificial intelligence (AI) and machine learning, and 6) Big Data Analysis. Dr. Tsai has developed and successfully implemented the complex, large-scale, GIS-based, Risk-based Georgia Pavement Management System (GPAMS) for Georgia Department of Transportation to assess, preserve, and manage its 18,000 centerline miles of highway over the past 20 years. This system includes modules of computerized data collection, performance forecasting, M&R determination, a risk-based M&R project prioritization, and resource optimization. Dr. Tsai was selected as a Chinese Changjiang Scholar in 2009 in recognition of his research on applying sensor and information technology to infrastructure asset management. Dr. Tsai currently serves in the technical committee of the United States National Cooperative Highway Research Program (NCHRP 20-102 (06) Road Markings for Machine Vision, one of 14 Connected and Automated Vehicles research projects sponsored by the USDOT. Dr. Tsai also 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 or processing and analyzing big NDS data, from 2008 to 2015. He is also 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.
With the advancement of sensor technologies, it become feasible to collect the large-scale in-field detailed infrastructure health condition data, using emerging sensing technologies to gain better insight understanding of the large-scale in-filed infrastructure behavior. An intelligent sensing system will be presented, using emerging sensing technologies, including 2D Imaging, 3D Laser, LiDAR, UAV, smart phones, and GPS/GIS Technologies with artificial intelligent (AI) to automatically detect pavement surface distress, including potholes, rutting, cracking, raveling, etc. An innovative crack fundamental element (CFE) model, based on a topological representation is also developed to support the big sensor data analyses of crack classification, deterioration, and diagnosis. Successful implementation of Automatic Sign Inventory and Pavement Condition Evaluation on Georgia’s Interstate Highways (selected as the 2017 AASHTO High Value Research Award, national award) will be presented. Utilization of emerging 3D laser and LiDAR technologies with GPS/GIS technologies, along with simulation, to automatically collect roadway safety condition for identifying and predicting dangerous roadway sections for proactive roadway safety improvement, like High Friction Surface Treatment (HFST) application will also be presented.