A Stacking Ensemble-Based Framework for Predicting the Compressive Strength of Microwave-Cured Geopolymers

Microwave curing offers energy-efficient geopolymer synthesis, yet optimizing compressive strength remains challenging due to complex variable interactions. This study develops an interpretable stacking ensemble surrogate model to predict the strength of microwave-cured geopolymers. A literature-derived dataset of 235 observations was systematically compiled from literature (2010–2025), covering diverse aluminosilicate precursors, activator concentrations, and curing parameters. The framework integrates linear, kernel, and tree-based base models via a linear meta-learner to enhance generalization. Unlike conventional models, source-aware validation was implemented to ensure reliability across heterogeneous studies. The stacking ensemble significantly outperformed standalone models, achieving a coefficient of determination (R2) of 0.957 and a low RMSE of 5.64 MPa. Crucially, the model demonstrated high reliability through rigorous residual analysis and noise sensitivity stress tests, confirming stable performance across the entire strength range. Interpretability analyses using SHAP and partial dependence plots identified curing time, microwave power, and specimen size as dominant factors governing strength. These dependencies adhered to established geopolymerization kinetics, yielding physically reasonable response surfaces. This work demonstrates that stacking ensemble models serve as reliable statistical surrogate tools (not mechanistic simulators) for preliminary experimental screening within the investigated parameter space. The framework has not been validated against genuinely independent experimental campaigns or industrial-scale microwave curing conditions, and should be treated as a literature-based surrogate model rather than a deployment-validated predictive tool.