Research on bridge structural damage detection based on convolutional and long short-term memory neural networks

In order to improve the bottlenecks in traditional methods about the combined extraction of spatio-temporal correlation features and structural damage detection, this paper combined the data characteristics of acceleration vibration signal of the structural health monitoring to reduce the structural damage detection to the problem of multivariate time series classification. This paper proposes a novel structural damage detection approach based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) neural network models. Taking the acceleration vibration response data obtained by structural health monitoring as input, the correlation features between sensors in multiple time frames is extracted by CNN model, and then the feature matrix is input into the LSTM model which uses Softmax as output layer to further extract time-related features and classify the structural damage patterns. An actual monitoring dataset obtained from a bridge scaled-model is employed as the experimental context. The experimental results verify the advantages of the proposed method in terms of accuracy, precision, recall and F-scores. © 2020, Central South University Press. All rights reserved.