Research on damage identification of large-span spatial structures based on deep learning

Large-span spatial structure damage identification is a challenging element of structural health monitoring. Compared with other buildings such as bridges and frames, space structures are characterized by large spans, many degrees of freedom and complex structures. Therefore, this paper proposes a new step-by-step damage identification method for spatial structures based on vibration signals. The method uses recurrence plot to process the structural vibration response to obtain nonlinear features. Through the nonlinear features reacting to different damage conditions of the structure and introducing convolutional neural network to realize the classification recognition problem under different damages. The feasibility analysis of step-by-step identification of damaged nodes and damaged rods is carried out with an orthogonal orthotropic quadrangular cone mesh structure model as an example. The optimized model training methods of data augmentation and migration learning are also introduced. An overall recognition accuracy of more than 89.7% is obtained. In order to realize the application of the proposed loss identification method in practical engineering, an operable GUI interface is constructed by encapsulating with programming technology. Afterwards, the complete step-by-step damage identification method from substructure to rod was verified by combining field tests and numerical simulations using a single-layer column surface mesh shell model consisting of 157 nodes and 414 rods. The results show that the damage recognition method has more than 85% recognition accuracy for structural damage. To explain the effectiveness of the convolutional neural network model training visualization of the recognition image features is performed using class activation heat maps.