Assessment of Shear Strength and Failure Mechanisms in Exterior Reinforced Concrete Beam–Column Joints Using Machine Learning and Explainable Artificial Intelligence

Abstract

The seismic performance of reinforced concrete (RC) beam–column joints depends on both shear strength and failure mechanisms, the assessment of which remains challenging because of complex interactions among geometric, material, loading, and reinforcement parameters. This study presents a data-driven framework for assessing the shear strength and failure mechanisms of exterior RC beam–column joints. A database comprising 210 experimental specimens was systematically compiled from published studies. Seventeen input variables were selected based on structural mechanics, seismic design provisions, and previous experimental investigations. Machine learning models were developed for shear strength prediction and failure mode classification. SHAP was employed to interpret the trained models, while symbolic regression derived an interpretable design-oriented equation. On the independent test set, XGBoost achieved the highest shear strength prediction (R2 = 0.973, RMSE = 40.09 kN), whereas the Support Vector Machine achieved 80.5% classification accuracy. The results indicate that the governing parameters for failure mechanisms differ from those controlling shear strength. Joint shear capacity was primarily influenced by geometric dimensions and longitudinal reinforcement ratios, whereas axial load ratio and joint transverse reinforcement had a greater influence on failure mechanisms. These findings highlight the importance of simultaneously assessing shear strength and failure mode in RC beam–column joints.