Seismic Response Prediction of a 2D Single-Story Steel Frame Using Physics-Guided Support Vector Regression Ensemble

Predicting the nonlinear seismic response of structures that have entered the plastic range under strong ground motions is severely constrained by data scarcity and computational cost. In this article, to address this dual challenge, we propose a physics-guided ensemble model based on Support Vector Regression. A finite element model of a single-story steel structure was created, and 500 nonlinear time-series analyses were generated using Incremental Dynamic Analysis for 50 different natural ground motions, at 10 levels of PGA intensity. Using an innovative feature engineering strategy, the 16 original ground motion parameters were decomposed into intensity, waveform and interaction features, thereby expanding the input space to 47 physically meaningful dimensions. Subsequently, the 35 features with the greatest information richness were extracted using a selection process based on mutual information. A systematic comparison with benchmark models demonstrated that Support Vector Regression (SVR) with Radial Basis Function (RBF) kernels offered significantly superior performance to Deep Learning with a reduced number of samples for this task, thus confirming the superiority of the structural risk minimization principle under conditions of limited data. Furthermore, the proposed two-level stacked ensemble achieved the lowest Mean Absolute Error among all evaluated models, demonstrating improved robustness in reducing prediction deviations and suppressing extreme errors in nonlinear seismic response estimation. These results demonstrate that the combination of physics-guided feature engineering and kernel-based learning provides an efficient surrogate approach for rapid seismic response prediction of steel structures under previously characterized ground-motion conditions.