This paper presents a data-driven framework for predicting the compressive strength of recycled aggregate concrete (RAC) under design constraints, an environmentally sustainable construction material. The proposed method uses a LightGBM regression model integrated with multi-objective optimisation via the NSGA-II algorithm, improving prediction accuracy, robustness, and interpretability. While LightGBM is compared against XGBoost, CatBoost, and a stacking-based ensemble for benchmarking purposes, all reported results, including optimisation, uncertainty analysis, and SHAP-based feature importance, correspond exclusively to the final LightGBM-based model, ensuring clarity and consistency. A tolerance-based accuracy criterion of 10\% is adopted to reflect practical engineering requirements. The model development and validation are carried out using 535 concrete mix samples obtained from experiments, representing a wide range of material proportions and curing ages. The proposed LightGBM–NSGA-II method shows very consistent and trustworthy results. It achieved an average cross-validation R² of 0. 9859, an adjusted R² of 0. 9604, a tolerance-based prediction accuracy of 98. 08%, and a cross-validation accuracy of 94. 29%. Prediction errors remain low, with an RMSE of approximately 12. 2 MPa and an MAE of approximately 9. 3 MPa. Reliability is confirmed by a Prediction Interval Coverage Probability of 97. 87% and a Pareto dominance count of zero. SHAP and gain analysis identify the water-binder ratio, curing age, and superplasticiser content as the most influential factors. Statistical validation using paired t-tests and Wilcoxon signed-rank tests supports the method’s stability. Therefore, the method is suitable for the sustainable concrete mix design.
Yogi et al. (Tue,) studied this question.
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