Screening of critical intensity measures for rapid structural seismic response prediction based on machine learning

The challenge in applying machine learning for rapid structural seismic response prediction lies in establishing a reliable mapping between seismic intensity measures (IMs) and damage indicators. Current IMs are often inadequate due to the complex, non-linear interaction of multiple seismic factors, including intensity, source mechanism, and pulse effects, making single-indicator quantification difficult. This study addresses this gap by considering three distinct seismic wave types (near-field, far-field, and pulse waves) and establishing six multiple degrees of freedom (MDOF) structural models with varying periods in OpenSees. We selected maximum inter-story drift and maximum floor acceleration as critical damage indicators. An Extremely randomized trees (ET) algorithm was then utilized to develop the structural response prediction model. Through feature importance ranking, shapley additive explanations (SHAP) value analysis, and sensitivity analysis, the key IMs governing the structure’s maximum response were systematically identified. The efficacy of the proposed IMs was subsequently validated using three steel frame structures with different periods. The results demonstrate a significant improvement in the accuracy of the machine learning prediction model. The proposed IMs can achieve the same or higher prediction accuracy with fewer parameters. The prediction accuracy of the interstory drift ratio is improved by 34.87%, while the prediction accuracy of floor acceleration is enhanced by 26.58%, compared to previous studies using the same number of IMs. These findings provide crucial guidance for the selection of optimal IMs in machine learning applications for structural seismic response prediction.