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Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

Yu Zhang, Zhihua Xiong*, Zhuoxi Liang, Jiachen She, Chicheng Ma

College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, 712100, China

* Corresponding Author: Zhihua Xiong. Email:

(This article belongs to this Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)

Computer Modeling in Engineering & Sciences 2023, 136(1), 447-469.


A huge number of old arch bridges located in rural regions are at the peak of maintenance. The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge, owing to the absence of technical resources and sufficient funds in rural regions. There is an urgent need for an economical, fast, and accurate damage identification solution. The authors proposed a damage identification system of an old arch bridge implemented with a machine learning algorithm, which took the vehicle-induced response as the excitation. A damage index was defined based on wavelet packet theory, and a machine learning sample database collecting the denoised response was constructed. Through comparing three machine learning algorithms: Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (R.F.), the R.F. damage identification model were found to have a better recognition ability. Finally, the Particle Swarm Optimization (PSO) algorithm was used to optimize the number of subtrees and split features of the R.F. model. The PSO optimized R.F. model was capable of the identification of different damage levels of old arch bridges with sensitive damage index. The proposed framework is practical and promising for the old bridge’s structural damage identification in rural regions.


Cite This Article

Zhang, Y., Xiong, Z., Liang, Z., She, J., Ma, C. (2023). Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach. CMES-Computer Modeling in Engineering & Sciences, 136(1), 447–469.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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