Data-driven prediction of drift capacity for reinforced concrete shear walls using tree-based machine learning models

Abstract

Reinforced concrete shear walls are essential components of seismic-resistant structures, yet accurately predicting their drift capacity remains challenging due to the nonlinear interaction among geometric, material, and loading parameters. Traditional empirical or analytical models often fail to capture these complexities, motivating the use of machine learning (ML) as a data-driven alternative. In this study, a dedicated experimental database of 208 reinforced concrete shear walls with rectangular and I-shaped cross-sections was derived from the ACI 445B Shear Wall Database. The selected input parameters include the wall length-to-thickness ratio, normalized shear coefficient, axial load ratio, transverse reinforcement spacing-to-diameter ratio, ultimate-to-yield strength ratio of boundary reinforcement, longitudinal reinforcement ratio in boundary and web regions, and transverse reinforcement ratio. Three tree-based machine learning regressors were optimized using nested cross-validation. To improve predictive accuracy, their outputs were combined into a stacking ensemble. The dataset was divided into a development subset and an independent hold-out test subset, with model development and hyperparameter optimization performed exclusively within the development data. In addition, the predictive capability of the machine learning models was compared with the empirical formulation proposed by Abdullah and Wallace. The results show that the ML-based approaches provide strong predictive performance on independent test data. Among the considered models, the Stacking Ensemble achieved the best overall results, highlighting the potential of ensemble learning to capture the complex nonlinear relationships governing the drift capacity of reinforced concrete shear walls. The findings support the use of data-driven models as complementary tools to traditional empirical approaches for deformation-capacity prediction.