Seismic response prediction of composite plate shear walls- concrete filled (C-PSW/CF) using machine learning methods

Abstract

In the seismic design of composite plate shear walls filled with concrete, referred to as C-PSW/CF, cyclic backbone curves provide invaluable information for effectively withstanding seismic disturbances. The curves evaluate lateral strength, ductility, and energy dissipation capabilities, and guide performance-based design strategies. This study harnesses machine learning (ML) techniques to predict critical parameters of the cyclic backbone curve of C-PSWs/CF. Various types of existing C-PSWs/CF from the literature were categorized for this analysis. ML models, comprising Random Forests (RF), Support Vector Regression (SVR), eXtreme Gradient Boosting (XGB), and Gaussian Process Regression (GPR), were developed to facilitate the predictive tasks. The findings indicate that all models demonstrate robust performance in predicting maximum strength and ultimate strength, yielding R2 scores close to 1.00 for training and testing, and exceeding 0.90 for cross-validation. Additionally, RF, SVR, and XGB exhibit commendable accuracy in predicting yield strength and its corresponding drift ratio.