Structural health evaluation of arch bridge by field test and optimized BPNN algorithm

Abstract

Arch bridges play an important role in rural roads in China. Due to insufficient funds and a lack of management techniques, many rural arch bridges are in a state of disrepair, unable to meet the increasing transportation needs. Thus, it is of great significance to develop a set of rapid and economic damage identification procedures for the management and maintenance of old arch bridges. Sanliushui Bridge, located in Chenggu County, Hanzhong, is selected as a model case. Field tests and numerical simulations were carried out to identify the damage states of Sanliushui Bridge. Wavelet Packet Energy change Rate Sum Square (WPERSS), a damage identification index based on wavelet packet analysis method was implemented to process the measured data of the load test and the simulated data of the numerical calculation model with assumed damage. Back Propagation Neural Network (BPNN), Genetic Algorithm-based BPNN (GA-BPNN), Particle Swarm Optimization Algorithm-based BPNN (PSO-BPNN) approaches and test data analysis are adopted to compare the measured data with the simulated data to quantitatively identify the damage degree of the selected bridge. By comparing the results of the two methods mentioned above, it is found that the proposed damage identification approach realized a precise damage identification of the selected arch bridges

Keywords

• arch bridge
• wavelet packet
• damage identification
• back propagation neural network
• test
• particle swarm optimization