Analysis of bridge vibration response for identification of bridge damage using BP neural network

Abstract

Abstract In this article, the authors propose a method to identify the bridge damage using a backpropagation (BP) neural network. It uses bridge vibration response to solve the accuracy of bridge damage. A particle swarm optimization algorithm based on chaotic mutation is adopted to perform chaotic mutation operations and make the group jump out of the local optimum. CPSO (particle swarm optimization algorithm based on chaotic variation) algorithm can make up for the BP neural network model, easy to fall into the shortcomings of local optima, so the author will combine the two algorithms and discuss the environmental data of the bridge. Establishing a finite element model of the bridge through actual analysis, through data comparison, comparing the frequencies of the intact stages with the frequencies of the damaged stages, and verifying the neural network with random samples, for the degree of bridge damage, we get the root mean square error m s e mse and the correlation coefficient r. The result shows that the root mean square error m s e = 0.003196 mse=0.003196 , and the correlation coefficient r = 0.9654 r=0.9654 . There are only a few individual points; it seems that the relative error is relatively large. The rest of the fit is basically the same; it can meet the factors of vibration through the environment and perform damage identification for the structural damage monitoring of the bridge. Using the BP neural network model optimized by chaotic particle swarms, combined with the modal analysis of environmental vibration, it can be used in the monitoring of the health structure of the bridge, plays a certain recognition effect, and provides a new technical idea.