Data-driven modeling of compressive strength in sustainable self-compacting concrete incorporating recycled aggregates using ensemble learning techniques

Abstract

Abstract This study develops a robust framework for estimating the compressive strength of self-compacting concrete (SCC) incorporating recycled aggregates using supervised machine learning (ML) techniques. A comprehensive experimental database comprising 582 concrete mix designs was used, encompassing diverse input variables including binder content, water, coarse and fine aggregates, recycled aggregate proportion, superplasticizer dosage, and curing time. Seven ML algorithms—XGBoost, CatBoost, AdaBoost, Extra Trees, Bagging Regressor, K-Nearest Neighbors, and Radius Neighbors—were systematically trained using a stratified 70/15/15 data split and optimized via grid search with five-fold cross-validation. Model performance was evaluated using coefficient of determination (R 2), root mean squared error, and MAE across training, validation, and testing datasets. Among all models, XGBoost demonstrated the highest accuracy, achieving an average R 2 of 0.9799, RMSE of 2.87 MPa, and mean absolute error of 1.97 MPa. The Permutation Feature Importance analysis revealed that binder content, water, and coarse aggregate were the most influential predictors of strength. This study confirms that ensemble ML models, particularly XGBoost, can reliably predict the compressive strength of SCC with recycled aggregates, while offering transparent insights into material behavior. The results provide a valuable tool for sustainable mix design optimization and practical implementation in eco-efficient concrete construction.