Prediction compressive strength of cement-based mortar containing metakaolin using explainable Categorical Gradient Boosting model

Although machine learning models have been employed for the compressive strength (CS) of cement-based mortar containing metakaolin, it is difficult to understand how they work due to “black-box” nature. In order to explain the involved mechanism, Categorical Gradient Boosting (CatBoost) model with feature importance, feature interaction, partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP) is proposed in this paper. A dataset consisting of 424 samples with six input variables is used to build the CatBoost model, which has optimal performance by tuning a set of seven hyper-parameters using sequential model-based optimization. Five quantitative measures (R2, MAE, RMSE, a10-, a20-index) are employed to evaluate the accuracy and the obtained results are superior to the previous study. It is from feature importance that the most significant input variable involving the CS is water-to-binder ratio, followed by age of specimen and cement grade. The strongest feature interaction is between water-to-binder ratio and metakaolin. A comprehensive parametric study is carried out via SHAP and PDP to investigate the effects of all input variables on the CS of cement-based mortar.