Machine learning-based surrogate resilience modeling for preliminary seismic design

Early decisions made during the preliminary seismic design, particularly regarding the stiffness and strength of the selected lateral force-resisting system, have a significant impact on seismic resilience. However, the effects of these crucial decisions on resilience are not explicitly assessed until the end of the routine code-based design. The indirect design approach can bring about substantial design iterations that involve computationally expensive resilience assessment. For the support of direct resilience-informed design, this paper employs machine learning techniques to develop a surrogate model that maps site and building features to resilience indices. The surrogate model aids design practitioners in selecting satisfying design combinations of stiffness and strength in the preliminary seismic design. It provides real-time feedback on the resulting resilience, drastically reducing the time and effort required for iterative design revisions. To demonstrate the effectiveness of the proposed approach, a data set of 10,000 steel momentresisting frames (SMRFs) with wide-ranging design combinations is created; additionally, six regression algorithms, including linear regression, support vector regression, multi-layer perceptron, k-nearest neighbors, decision tree, and extreme gradient boosting (XGBoost), are used to establish the surrogate model. Among the six algorithms, XGBoost performed the best in terms of the lowest RMSE (0.031) and the highest R2 (0.97) for the test set. Identified by the SHapley Additive exPlanations (SHAP) approach, the seismic hazard-related design basic acceleration and the stiffness-related building period coefficient have the most significant impact on the prediction of XGBoost. This enables a physical and quantitative understanding of the surrogate estimates.