Damage identification based on convolutional neural network and recurrence graph for beam bridge
Haoxiang He, Jia-cheng Zheng, Lican Liao, Yan-jiang Chen
Abstract
Traditional statistical pattern identification methods, such as artificial neural network and support vector machine, have limited ability to identify minor damage of bridges. Deep learning can mine the inherent law and representation level of sample data. As a typical algorithm of deep learning, convolutional neural network is a feedforward neural network with deep structure and convolution calculation, and its ability of image identification is very outstanding. The recurrence graph of structural response can reveal the internal structure, similarity, and damage information. The original structure response signal involves the coupling vibration of vehicle and bridge is filtered and reconstructed by wavelet packet, and then the recurrence graph of different damage cases is obtained, which is used as the input image of convolutional neural network as a new type of damage feature; thus, a damage identification method based on convolutional neural network and recurrence graph is established. The results of numerical simulation and model experiment show that the recurrence graph contains more damage information; compared with the traditional statistical pattern identification methods, convolutional neural network can achieve more accurate feature extraction and identification through intelligent learning layer by layer, so as to realize more accurate identification of damage location and damage degree.