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


SALEH R. A. A., Hassan A., Al-Sameai H., De Moura J., Ortega M., Saleh Z., ...Daha Fazla

MATERIALS TODAY COMMUNICATIONS, cilt.51, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 51
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.mtcomm.2026.114883
  • Dergi Adı: MATERIALS TODAY COMMUNICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Compendex, INSPEC
  • Kocaeli Üniversitesi Adresli: Hayır

Özet

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.