Saleh, Radhwan A.A.Hassan, AmerAl-Sameai, HabebMoura, Joaquim deOrtega Hortas, MarcosSaleh, ZeadAlomayri, ThamerZhang, Chunwei2026-04-072026-04-072026-02-23R. A. A. Saleh et al., «Data driven modeling and experimental validation of alkali activated materials for construction applications», Materials Today Communications, vol. 51, p. 114883, feb. 2026, doi: 10.1016/j.mtcomm.2026.114883.2352-4928https://hdl.handle.net/2183/47889[Abstract]: 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 R2 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 R2 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.eng© 2026 The Authors. Published by Elsevier Ltd.Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Low-carbon concreteMachine learning modelCompressive strengthSustainable constructionjournal articleopen access10.1016/j.mtcomm.2026.114883