Structural damage identification based on the wavelet scattering convolution neural network

Damage identification is one of the key issues in the field of structural condition assessment, which is of great importance to ensure structural safety. The deep learning algorithm has led to many breakthroughs in vibration-based structural damage identification, but it is still an urgent technical challenge to obtain the key information of structural damage from massive amounts of data. A multi-type structural damage identification model was proposed based on the one-dimensional - convolutional neural network (1D - CNN) deep learning. The wavelet scattering transform was used to replace the convolutional filter in the first layer of the 1D - CNN architecture. The scattering coefficients were used to achieve dimensionality reduction and feature extraction of the original data in the input layer, and the CNN convolutional layer, activation layer and pooling layer were combined to achieve feature enhancement processing of monitoring data. The 1D - CNN fully-connected layer and Softmax function were combined to classify the feature data, thus realizing the location and quantitative identification of multi-type structural damages. The above frame was verified by two numerical models of a steel truss structure and a cable-stayed bridge. The results show that compared with the normal convolutional neural network model, the accuracy of structural damage identification based on the wavelet scattering based convolutional neural network is significantly improved, and the accuracy of damage classification is more than 95. 0%. In addition, with the increase of the proportion of environmental noise in sensor data, the accuracy of the wavelet scattering convolutional neural network damage classification slightly decreases but still has high accuracy, indicating that the method has strong robustness and anti noise ability. © 2023 Chinese Vibration Engineering Society. All rights reserved.