报告人简介:

Dr.Yu Qianis an Associate Professor at the University of South Carolina. He obtained his Bachelor's degree in Structural Engineering from the Huazhong University of Science and Technology, a Master's degree with honors in Geotechnical Engineering from the University of Kansas, and another Master's degree in Theoretical and Applied Mechanics and a Ph.D. in Civil Engineering, both from the University of Illinois at Urbana-Champaign.

He has been engaged in transportation geotechnics research for 15 years, including heavy haul, urban transit, and high-speed railroad. His research interests mainly focus on intelligent railroad infrastructure design, inspection, and maintenance, aggregate behavior, DEM and CFD simulation, computer vision, image analysis, artificial intelligence, and edge computing. He has published more than 80 journal articles, 40 conference papers, and 10 technical reports. Dr. Qian serves as the Secretary of the ASCE Rail Transportation Committee and is a committee member of the Transportation Research Board (TRB) and the American Railway Engineering and Maintenance of Way Association (AREMA). He has secured over 5 million US dollars in research funding, with over 3.5 million as the primary investigator, from sources such as the National Academy of Sciences, Federal Railroad Administration, Transportation Technology Center, Inc., and other state agencies and private sector organizations. His contributions to the field have been recognized through numerous awards, including the Best Paper Award from the Transportation Research Board, the Breakthrough Star Award from the University of South Carolina, Young Investigator Awards from the College of Engineering, and three consecutive Dean's Excellence Awards in Research (2021-2023).

报告摘要:

Railway engineering is an interdisciplinary field of engineering that encompasses various aspects of designing, constructing, and operating rail transport systems. With over 250,000 km of track, the United States operates the largest railway network in the world. Nearly 40% of the freight in the U.S. is transported through this rail network. Regular inspection and maintenance of railway tracks is critical for ensuring safe and efficient operations. Despite the significant demand for this work, there has been a lack of research and development in this area, leading to manual inspection processes and a heavy reliance on field experience for maintenance planning. This approach can be physically demanding, inefficient, and potentially inaccurate, and can sometimes result in accidents. To overcome the challenges posed by manual inspection processes, we have developed automated inspection methods for various applications to offer unprecedented efficiency and accuracy. This presentation will introduce our recent developments and applications of computer vision, artificial intelligence, and edge computing in the railway, transforming the track into an important component of the grand smart transportation system and connected community.