Abstract: H. He, W. Wang, L. Huang (2020)

To improve the deficiencies of the current traditional damage detection methods, such as the inadequate detection ability for local damage and minor damage, the convolution neural network based on deep learning method is proposed to improve statistical damage pattern recognition for bridge. According to the demand of the damage characteristic vectors of the convolutional neural network, the original structural responses of vehicle-bridge coupling vibration are regenerated by wavelet packet filtering and reconstruction, and the recurrence plots of different damage cases are obtained as the damage characteristics images and the input of convolution neural network according to recurrence analysis. On this basis, the corresponding calculation process and method for damage detection of bridge structure based on convolution neural network and recurrence plot are established.The damage simulation for a continuous beam bridge is carried out, including damage locations and degrees. The damage feature vectors such as frequency band energy of wavelet packet and recurrence plots are extracted, and damage detection based on multiple statistical pattern recognition algorithm is carried out. The results showed that the damage information in recurrence plots is more abundant compared with other feature vector, and the convolutional neural network can fulfill automatic extraction and distinguishing feature more accurately according to layer-by-layer intelligent learning, compared with the traditional damage detection methods, such as support vector machine and BP neural network. Hence, the damage detection method based on convolution neural network and recurrence plot can achieve more identification accuracy for damage location and degree.

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Intelligent Damage Detection for Bridge Based on Convolution Neural Network and Recurrence Plot