Summary of Research on Highway Bridge Vehicle Force Identification

Bing-Chen Yang, Yu Zhao, Tian-Yun Yao (), Yong-Jun Zhou, Meng-Yi Jia, Hai-Yang Hu and Chang-Chun Xiao
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Bing-Chen Yang: School of Highway, Chang’an University, Xi’an 710064, China
Yu Zhao: School of Highway, Chang’an University, Xi’an 710064, China
Tian-Yun Yao: School of Civil Engineering, Chang’an University, Xi’an 710061, China
Yong-Jun Zhou: School of Highway, Chang’an University, Xi’an 710064, China
Meng-Yi Jia: School of Highway, Chang’an University, Xi’an 710064, China
Hai-Yang Hu: School of Highway, Chang’an University, Xi’an 710064, China
Chang-Chun Xiao: School of Highway, Chang’an University, Xi’an 710064, China

Sustainability, 2024, vol. 16, issue 11, 1-17

Abstract: Vehicle force identification is one of the core technical problems to be solved urgently in the management of transportation infrastructure, and it has also been a research hotspot in recent years. To promote the application of vehicle force identification technology on bridges and explore its development direction, the development status of indirect vehicle force identification methods based on bridge response is reviewed during this study. The basic theories of two major methods, including bridge weigh-in-motion (BWIM) and moving force identification (MFI), are described in detail in this study, and then, the key technical principles of bridge force identification are revealed. Secondly, the development status of BWIM in recent years is reviewed from three aspects, including test accuracy, applicability and test efficiency. Combined with a variety of theories, the current status of MFI is analyzed from the establishment of the solution to the equation. Finally, the development direction of an artificial neural network and machine vision technology are prospected in this study. The BP neural network has good self-learning ability and self-adaptive ability, but the algorithm needs to be improved. The identification method based on machine vision represents the current development direction in vehicle force identification, with great potential.

Keywords: bridge engineering; force identification; bridge weigh-in-motion; moving force identification; neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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