Efficient Ensemble Learning-Based Models for Plastic Hinge Length Prediction of Reinforced Concrete Shear Walls

Abstract

Reinforced concrete shear wall (RCSW) significantly improves the seismic performance of buildings. Accurate estimation of the plastic hinge length (PHL) of RCSWs is crucial as it significantly impacts the plastic deformation, ultimate displacement, and ductility capacity of RCSWs. This study aims to develop practical machine-learning (ML) models for PHL prediction of RCSWs. For this purpose, 721 data of nonplanar and rectangular RCSWs were utilized. Deep neural network-based ensemble learning models including Simple Averaging Ensemble (SAE), Stacking Ensemble (SE), Snapshot Ensemble (SSE), and Deep Forest (DP), were leveraged. Meanwhile, inherently ensemble-learning-based (IELB) algorithms including the XGBoost, RandomForest, CatBoost, HistGradientBoosting, AdaBoost, Bagging, ExtraTrees, and GradientBoosting regressor, and data-driven empirical equations were considered for comparison. The Taylor diagram and statistical comparison of the results revealed that the proposed SE model with the gradient boosting regressor (GBR) meta-learner (MAE=0.043, MSE=0.012, R2=0.916) outperformed all employed deep-and IELB-based ensemble algorithms as well as the empirical formulas for PHL of RCSWs. The SHapley Additive exPlanations-based model interpretation together with Sobol's sensitivity analysis results revealed that the wall length is the most crucial input variable, followed by the effective height and the axial load ratio.

Keywords

• reinforced concrete wall (rcsw)
• plastic hinge length (phl)
• machine learning
• ensemble algorithms
• gradient boosting regressor (gbr)
• shapley additive explanations (shape)