Bridge structural damage identification based on parallel CNN-GRU

Structural damage identification has been the focus of engineering fields, while the existing damage identification methods heavily depend on extracted hand-crafted features. Recently, due to the powerful feature learning capability of deep learning, it has been widely used in structural damage identification. However, those methods only consider the local dependence or temporal relation of data. Thus, in this paper, a structural damage identification method by combining the convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed. The CNN model is used to extract the local dependence of data, and the GRU model is used to extract the temporal feature of data. These two extracted feature matrices are spliced horizontally to a fused eigenvector. The eigenvector is input to the final softmax classifier layer to identify the structural damage state. Experiments on a scale model of the three-span continuous rigid frame bridge shown that the CNN-GRU model performs significantly better than CNN, LSTM, and GRU models for structural damage identification.

Automated summary

Deep learning has been widely used in structural damage identification, and the combination of CNN and GRU models for feature extraction has shown significantly better performance in experiments for structural damage identification.