Application of machine learning boosting and bagging methods to predict compressive and flexural strength of marble cement mortar

Compressive (CS) and flexural strength (FS) of sustainable mortar made from waste materials were estimated using machine learning (ML) tools. Ensemble ML techniques, including extreme gradient boosting (XGB) and bagging regressor (BR), were utilized to accomplish the study goals. The outcomes of the estimation models were compared with target values, and statistical checks were performed to assess and compare the models. Additionally, in order to determine the influence of mortar constituents on CS and FS estimation of mortar, the Shapley Additive exPlanations (SHAP) method was implemented. XGB exhibited superior predictive capabilities for the CS and FS of mortar compared to the BR approach, as evidenced by R2 values of 0.98 and 0.94 for the CSXGB and FS-XGB models, respectively. Statistical validation checks provided additional evidence that the ML models were effective in estimating the strength of sustainable mortar. The results of the SHAP study showed that the most promising components that contributed favorably to strength were testing age, fly ash, and rice husk ash; however, waste-derived cement had a negative impact. Establishing models that can determine the CS and FS of mortar for different values of the input parameters could save time and money compared to doing the tests in a lab.