Machine learning-based prediction of seismic response of elevated steel tanks

Abstract

Seismic response analysis assesses structural safety and integrity during earthquakes and guides design for optimal performance, compliance with regulations, and risk mitigation. This study proposes a novel approach utilizing machine learning (ML) techniques to predict the seismic response of elevated steel tanks accurately. ML is a powerful tool that can handle complexity, improve accuracy, provide data-driven insights, and optimize designs for predicting seismic responses of structures. Moreover, this study evaluates various ML models containing Random Forest, Support Vector Regression, Adaptive Boosting, LightGBM, Histogram-based Gradient Boosting, XGBoost, CatBoost, Bagging Regression, and Decision Trees to identify the most effective predictive model. In general, the developed ML-based predictive models provide a promising tool for engineers involved in the seismic design and retrofitting of elevated steel tanks, aiding in optimizing designs and enhancing structural safety and resilience against seismic hazards. As a result, the HGB model obtained the most suitable performance compared to other developed models with higher R² and lower RMSE. In addition, a sensitivity analysis was performed based on SHAP values, and it was found that the height of the liquid and the intensity measure were the most influential variables in the seismic response of the tank.