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

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.

Automated summary

Due to limited technical resources and funds in rural regions, the health monitoring technology for long-span bridges is not suitable for small-span bridges. An economical, fast, and accurate damage identification solution is urgently needed. The authors proposed a damage identification system using a machine learning algorithm for old arch bridges, which utilized vehicle-induced response as excitation and defined a damage index based on wavelet packet theory. After comparing three machine learning algorithms (BPNN, SVM, and RF), the RF model demonstrated better recognition ability. The authors also optimized the RF model using Particle Swarm Optimization (PSO) to identify different damage levels of old arch bridges with a sensitive damage index. The proposed framework is practical and promising for structural damage identification in old bridges in rural regions.