Machine Learning-Driven Comparative Prediction of Compressive Strength in True Geopolymer and Hybrid Alkali-Activated Cement Systems

Abstract Ordinary Portland cement (OPC) production accounts for approximately 7–8% of global CO2 emissions, motivating the development of sustainable binders such as metakaolin-based geopolymers and hybrid alkali-activated cements (AACs). However, accurate prediction of compressive strength in these systems remains challenging because of nonlinear interactions among precursor composition, activator chemistry, water-related parameters, and curing conditions. This study develops a reliability-oriented machine-learning framework using literature-derived datasets comprising 223 metakaolin-based geopolymer mixtures and 262 hybrid AAC mixtures. Five tree-based ensemble algorithms were evaluated using five-fold GroupKFold validation, with literature source used as the grouping variable to reduce the risk of study-level information leakage. The framework further incorporated fold-wise hyperparameter optimization, inverse-frequency sample weighting during model fitting, source-wise sensitivity analysis, and leave-one-source-out evaluation. Under grouped internal validation, CatBoost achieved the best performance for the metakaolin-based geopolymer dataset (R2 = 0.8211 ± 0.1275, RMSE = 5.773 ± 2.333 MPa, MAE = 4.131 ± 1.543 MPa), whereas Gradient Boosting performed best for the hybrid AAC dataset (R2 = 0.8876 ± 0.0708, RMSE = 3.333 ± 1.181 MPa, MAE = 2.366 ± 0.737 MPa). TreeSHAP analysis revealed distinct model-derived feature associations: hybrid AAC predictions were most strongly associated with GGBFS content, followed by activator- and curing-related variables, whereas metakaolin-based geopolymer predictions were most strongly associated with the H2O/Na2O ratio, aggregate proportion, activator chemistry, and curing conditions. These SHAP-derived patterns represent statistical associations learned by the models rather than direct evidence of physical causation or reaction mechanisms. Overall, the proposed framework provides a transparent approach for preliminary compressive-strength screening and experimental prioritization of sustainable alkali-activated binders.