Base‐isolation design of shear wall structures using physics‐rule‐co‐guided self‐supervised generative adversarial networks

Seismic isolation can significantly improve the seismic resilience of buildings, resulting in a growing demand for seismic isolation designs. Meanwhile, the deep generative network‐based intelligent design can significantly increase scheme design efficiency. However, the performance of existing intelligent scheme designs is constrained by data quality and quantity. The limited availability of isolation design data hinders the development of intelligent seismic isolation design. Therefore, there is an emerging demand to establish an intelligent scheme design method that is free from data constraints and that can learn the physical mechanism and design rules. Consequently, this study proposes a physics‐rule‐co‐guided self‐supervised generative adversarial network (GAN) that can generate the layout and parameters of seismic isolation bearings by inputting the layout drawings of the shear wall structures. The critical physics‐rule‐co‐guided network model consists of a physics estimator, rule evaluator, discriminator, and design generator. The physics estimator is a deep neural network‐based surrogate model for predicting the mechanical response of an isolated structure, whereas the rule evaluator is a tensor operation‐based loss calculator that considers design rules. Furthermore, the proposed GAN model masters the schematic design ability of the seismic isolation of shear wall structures through multiphase hybrid learning of the pseudo‐labels, physical mechanism, and isolation design rules, obviating the need for ground‐truth data. Case studies also prove the rationality of the method, where the design results can effectively meet the code requirements and reduce the seismic response of the structure.