Explainable machine learning model for load-carrying capacity prediction of FRP-confined corroded RC columns

This paper proposed a novel explainable machine learning (ML) model to predict the axial load-carrying capacity (Pmax) of FRP-confined corroded RC columns utilizing the eXtreme Gradient Boosting (XGBoost) algorithm and Shapley Additive exPlanations (SHAP) technique. The XGBoost predictive model was constructed based on thorough database of experimental tests for 285 FRP-confined corroded RC columns collected from existing studies and those performed by the authors. Twenty parameters were taken into account as critical input variables to develop the predictive model. SHAP technique was employed for performing the importance evaluation and interpreting the prediction performance of XGBoost model. Additionally, feasibility and effectiveness of the constructed XGBoost model were assessed by using several empirical design models and some other ensemble ML algorithms. The results indicated that, (i) the suggested XGBoost model was validated to be feasible to predict Pmax of FRP-confined corroded RC columns; (ii) the SHAP technique provided good explainability and interpretability to the XGBoost predictive model; (iii) the input variables could be comprehensively studied concerning the feature importance through SHAP technique, and the most important ones affecting the determination of Pmax of FRP-confined corroded RC columns were the gross sectional area of column, FRP thickness, elastic modulus of FRP, eccentricity ratio, corrosion rate, and concrete compressive strength; (iv) the prediction effectiveness and feasibility of the proposed XGBoost model were significantly superior to those of the existing empirical models and other ML algorithms, and the mean values of R2, RMSE, MAE, and MAPE of the XGBoost model were 0.978, 122 kN, 703.6 kN, and 7.7%, respectively; and (v) the recommended XGBoost model could offer the alternative approach to determine Pmax of FRP-confined corroded RC columns for design practices, in addition to the current mechanics-based design models.