The prediction of the compressive strength (CS) for sustainable concrete reinforced with graphene nanoplatelets (GNPs) is difficult as a result of nonlinear interactions between chemical composition, dispersion state, and curing conditions. To address this, an interpretable ensemble machine learning framework is developed to provide accurate predictions of CS. The major input parameters used are sand content, graphene diameters, graphene thicknesses, and percentages of GNP to sand (GNP%; w/w), water-to-cement ratio W/C, ultrasonication period UST time (s), curing age CA day(s), while the CS (in MPa) is the target output. The random forest (RF) and XGBoost (XGB) models are incorporated into two novel metaheuristic optimization techniques, the Drawer-based optimization algorithm (DOA) and the Giant Trevally Optimizer (GTO), to enhance hyperparameter tuning and generalization. For all models, DOA XGB hybrids are the most predictive, with testing R2 values up to 0.98; RMSE of around 2.9 MPa; MAE is approximately 2.0 MPa, and well over 97% within ±20% prediction error boundaries. The explainable artificial intelligence methodologies like Shapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), partial dependence plots, and Individual Conditional Expectation plots reveal curing age and graphene thickness as the dominant parameters. High strengths above 70 MPa are always achieved from higher curing age, w/c ratio (from 0.3 to 0.4), and graphene dosage (from 0.5 to 2.5%). A Python GUI is developed for efficient and accurate strength predictions suitable for practical applications. The proposed approach provides a robust, interpretable, and efficient alternative to extensive testing for GNP-reinforced concrete.
Sapkota et al. (Fri,) studied this question.