End-to-End Structural analysis in civil engineering based on deep learning

This paper presents DeepSNA (Deep Structural Nonlinear Analysis), the first general end-to-end computational framework in civil engineering that can predict the full range of mechanical responses of different structures based on deep learning. The proposed framework comprehensively considers intrinsic structural information and external excitations from both the data and the model. First, a data interface schema was carefully designed to eliminate manual interventions. Based on the distinctive characteristics of structural analysis, we proposed deep learning models for static and dynamic features that could identify underlying mechanical coupling relationships and historical dependencies. Moreover, we designed data augmentation algorithms to address the lack of data in real-world applications. Finally, we developed the DeepSNA framework and validated it with steel plate shear wall structures. The results indicated that the new framework was more accurate and had at least 1000 times greater computational efficiency than conventional numerical methods.