This study develops advanced machine learning (ML) algorithms to predict the tensile strength (Ft) of sustainable recycled aggregate (RA) concrete incorporating supplementary cementitious materials (SCMs—silica fume and fly ash) and polypropylene fibers (PPF). A dataset of 375 Ft results from the literature, characterized by ten input parameters (including cement content, natural and RA contents, SCM dosages, PPF percentage, water–cement ratio, superplasticizer content, and curing period), was used to train and validate two ML algorithms: Random Forest (RF) and Extreme Gradient Boosting (XGBoost). All models demonstrated high predictive accuracy, with results consistently aligning with experimental values, though the XGBoost model outperformed the RF model, achieving superior performance with R2 values of 0.9689 and 0.9632 for the training and testing datasets and lower RMSE and MAE values. To interpret the model decisions and uncover black-box insights. SHapley additive explanations (SHAP) analysis was employed, quantifying the global and local importance of each input variable on tensile strength prediction, revealing complex non-linear relationships and interactions. The findings highlight XGBoost as a robust tool for optimizing the mix design of complex sustainable concrete, while SHAP analysis revealed that curing period has the highest positive impact on predicting Ft, and W/C and RA adversely impact Ft, bridging the gap between data-driven predictions and practical engineering applications. The developed XGBoost model outperformed DNN, OGPR, and GEP in predicting. A graphical user interface (GUI) was developed to be used as a tool for predicting Ft of RA concrete containing SCMs and PPF. This approach facilitates the efficient development of high-performance, eco-friendly concrete with reduced experimental effort.
Alkharisi et al. (2025) studied this question.