Interpretable Machine Learning for the Shear Capacity of RC Corbels: A Validated, Application-Driven Model

This paper demonstrates the application of a robust machine learning methodology to develop an accurate and, critically, an interpretable data-driven model for RC corbel shear assessment. A primary focus of this work is the use of advanced explainability techniques to rigorously validate the model’s predictive logic against fundamental principles of structural mechanics, directly confronting the limitations of “black-box” approaches. To implement this framework, an extensive database of 515 experimental tests was assembled. Different machine-learning (ML) techniques, including Random Forest, AdaBoost, Support Vector Machine, and XGBoost, were systematically evaluated to define the optimal predictive model. The most accurate algorithm, XGBoost, was selected and optimized to achieve exceptional performance, with a coefficient of determination (R2) of 0.98 evaluated across the full database and a mean absolute relative deviation (MARD) of only 4%; on the held-out testing subset the model retains an R2 of 0.97 and a MARD of 15%, confirming that predictive performance does not degrade appreciably on unseen specimens. The predictive model was shown to be substantially more accurate and generalizable than current design approaches, including both ACI code provisions and other prominent analytical models from the literature. Crucially, the Shapley Additive exPlanations (SHAP) technique was used to rigorously interrogate the model’s predictive logic. The analysis showed that the model’s feature attributions are consistent with established structural mechanics, correctly identifying the governing influence of parameters like the shear span-to-depth ratio and reinforcement indices for distinct failure modes. This explainability analysis establishes that the learned associations agree with structural expectations; it does not by itself demonstrate mechanistic causality. The study provides a validated methodology for creating trustworthy ML models and indicates, subject to further validation, uncertainty quantification, and a clearly defined applicability domain, how such interpretable tools might complement existing design provisions.