Bridge Moving Load Identification: Key Technical Difficulties and Research Progress
Yuhang Song *
School of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450045, China.
*Author to whom correspondence should be addressed.
Abstract
Bridge moving load is the core variable load that controls bridge structural design, bearing capacity evaluation and fatigue life assessment. Moving Load Identification (MLI) is a key technology for Bridge Health Monitoring (SHM) and Bridge Weigh-in-Motion (B-WIM), which infers vehicle axle load, speed, wheelbase and load time history based on bridge dynamic responses. This paper systematically combs the theoretical framework, mainstream methods, key technical difficulties and engineering applications of bridge moving load identification, summarizes the research progress in regularization optimization, multi-sensor fusion, deep learning, vehicle-bridge coupling and other directions, comments on the applicable scenarios and limitations of existing methods, and looks forward to the development trends such as multi-modal perception, digital twin, uncertainty quantification and lightweight edge computing, so as to provide reference for bridge safety monitoring and intelligent maintenance.
Keywords: Bridge engineering, moving load identification, weigh-in-motion, vehicle-bridge interaction, health monitoring, regularization, deep learning