Reliable Compressive Strength Prediction of Self-Compacting Concrete with Recycled Coarse Aggregate Using an Interpretable Machine Learning Model (LightGBM) with Uncertainty Quantification
Abstract
Machine learning is increasingly used to predict the compressive strength of self-compacting concrete with recycled coarse aggregate (SCRCAC), with coefficients of determination of 0.81–0.87 reported in the literature. This paper first shows that the widely used reference dataset of 603 mixtures contains only 504 unique compositions, with 84 groups of identical and 21 contradictory ones, so that identical mixtures leak between the training and test sets. Under an objective, leakage-free evaluation (with the model re-tuned on the deduplicated dataset), the coefficient of determination drops to about 0.73, a correction that applies to all models on this dataset. We then propose an interpretable, hyperparameter-optimized LightGBM model that (i) reaches the level of the best published results under the standard protocol (seed-averaged five-fold cross-validation (CV) R2 = 0.813); (ii) provides a calibrated uncertainty interval for each prediction via split-conformal prediction, achieving an empirical coverage of 0.906 at the 90% nominal level; and (iii) remains fully explainable (SHAP (SHapley Additive exPlanations), partial dependence), with cement as the dominant predictor, followed by water and the mineral admixture. Under a 70/30 protocol averaged over 25 splits, it achieves an R2 = 0.794 ± 0.038 and a root mean squared error (RMSE) = 6.20 ± 0.48 MPa, exceeding all four machine learning models of the reference study. Aspects in which the reference study retains an advantage are also discussed.