A soft-computing-based modeling approach for predicting acid resistance of waste-derived cementitious composites

•Four ensemble M−L algorithms were employed to predict the C-S of C-M after acid attack.•A dataset of 234 points with eight input parameters was used for M−L modeling.•SHAP analysis determined the significance and interaction of input features.•Accurate predictions were made for the C-S of C-M after acid attack using M−L methods.•Bagging and random forest methods yielded more accurate results than gradient boosting and AdaBoost. This research aimed to build estimation models for the compressive strength (C-S) of cement mortar containing eggshell and glass powder after the acid attack using machine learning algorithms. A lab test data comprising 234 data points with 8 input factors was utilised for modelling. Four ensemble machine learning techniques, including gradient boosting, AdaBoost, random forest, and bagging, were employed to achieve the research's goals. In addition, to examine the influence and correlation of input factors, a SHapley Additive exExplanations (SHAP) analysis was conducted. The built estimation models well agreed with the lab test results based on R2 and the variance between actual and model estimated results (errors). Random forest and bagging exhibited superior prediction performance, with R2 of 0.982 and 0.983, respectively, than gradient boosting and AdaBoost, with R2 of 0.969 and 0.977, respectively. The comparative analysis of statistical measures also indicated superior accuracy of random forest and bagging, with mean absolute percentage error (MAPE) of 2.40%, than gradient boosting and AdaBoost, with MAPE of 2.90% and 2.60%, respectively. SHAP analysis exhibited that the highly influential factor for the acid resistance of glass and eggshell-based mortar was the 90-day C-S of the sample, followed by the quantity of glass powder, eggshell powder, sand, cement, water, superplasticizer, and silica fume.