Data driven modeling and experimental validation of alkali activated materials for construction applications

This study presents a robust machine learning framework for predicting the compressive strength of fly ash geopolymer concrete (FAGPC) using a comprehensive database of over 1000 mix designs. The core innovation is a two-stage residual learning architecture that combines a LightGBM base model with error-correcting layers (KNN, SVR, or Extra Trees) to capture complex, non-linear relationships between chemical ratios, aggregate proportions, and curing conditions. To ensure model reliability and prevent data leakage, a nested cross-validation strategy was employed, resulting in an average of 0.837 and RMSE of 6.05 MPa. Experimental validation on unseen specimens confirmed the framework’s predictive power, with the LightGBM-KNN hybrid achieving an of 0.866 and an MAE of 2.39 MPa. By significantly reducing the reliance on resource-intensive trial-and-error methods, this computationally efficient tool provides a validated pathway to accelerate the adoption of low-carbon materials in sustainable construction.