Hybrid surrogate model combining physics and data for seismic drift estimation of shear-wall structures

To address the issue of costly computational expenditure related to high-fidelity numerical models, surrogate models have been widely used in various engineering tasks, including design optimization. Despite the successful application of the existing surrogate models, physics-based models depend largely on simplifications and assumptions, which render parameter calibration challenging; whereas data-driven models require substantial data to reach their full potential, with their performance often being constrained in tasks when obtaining massive data is difficult. In this study, a hybrid surrogate model is proposed combining physics-based and data-driven models to rapidly estimate building seismic responses. The application of this model is exemplified through effective estimation of inter-story drift ratios (IDRs), being a critical factor in shear-wall structure design. Initially, a data augmentation technique and a parametric modeling procedure are introduced to significantly enhance the dataset diversity. Subsequently, a task decomposition strategy is proposed to effectively integrate a data-driven graph neural network (GNN) and a physics-based flexural-shear model. Additionally, the output layer and the loss function of the GNN are modified to enhance the estimation accuracy by eliminating fundamental errors. Results of numerical experiments indicate that the proposed hybrid model can complete IDR estimations in an average time of 0.56 s, with a mean absolute percentage error of 12.7%. This performance significantly surpasses that of existing purely data-driven and physics-based models. A case study shows that the efficiency of the proposed hybrid model is approximately 100 times greater than that of conventional finite element software. This enables an accurate assessment of the design compliance with code requirements. The results of this study can be applied to the design optimization of seismic-resistant building structures.