Seismic Response Prediction of Rigid Rocking Structures Using Explainable LightGBM Models

Ioannis Karampinis, Kosmas E. Bantilas, Ioannis E. Kavvadias, Lazaros Iliadis () and Anaxagoras Elenas
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Ioannis Karampinis: Lab of Mathematics and Informatics (ISCE), Department of Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Kosmas E. Bantilas: Institute of Structural Statics and Dynamics, Department of Civil Engineering Democritus University of Thrace, 67100 Xanthi, Greece
Ioannis E. Kavvadias: Institute of Structural Statics and Dynamics, Department of Civil Engineering Democritus University of Thrace, 67100 Xanthi, Greece
Lazaros Iliadis: Lab of Mathematics and Informatics (ISCE), Department of Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Anaxagoras Elenas: Institute of Structural Statics and Dynamics, Department of Civil Engineering Democritus University of Thrace, 67100 Xanthi, Greece

Mathematics, 2024, vol. 12, issue 14, 1-18

Abstract: This study emphasizes the explainability of machine learning (ML) models in predicting the seismic response of rigid rocking structures, specifically using the LightGBM algorithm. By employing SHapley Additive exPlanations (SHAP), partial dependence plots (PDP), and accumulated local effects (ALE), a comprehensive feature importance analysis has been performed. This revealed that ground motion parameters, particularly peak ground acceleration (PGA), are critical for predicting small rotations, while structural parameters like slenderness and frequency are more significant for larger rotations. Utilizing an extensive dataset generated from nonlinear time history analyses, the trained LightGBM model demonstrated high accuracy in estimating the maximum rotation angle of rigid blocks under natural ground motions. The study also examined the sensitivity of model performance to lower bound thresholds of the target variable, revealing that reduced feature sets can maintain predictive performance effectively. These findings advance ML-based modeling of seismic rocking responses, providing interpretable and accurate models that enhance our understanding of rocking structures’ dynamic behavior, which is crucial for designing resilient structures and improving seismic risk assessments. Future research will focus on incorporating additional parameters and exploring advanced ML techniques to further refine these models.

Keywords: rocking blocks; machine learning; LightGBM; explainability; SHAP (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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