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Comparative and Interpretable Machine Learning for Fly Ash Geopolymer Strength: Prediction, Validation and Optimization

  • Xiaoshuang Shi 1, 2, *,   
  • Haoxiang Hu 2,   
  • Zhenhua Duan 3,   
  • Yanlin Li 4

Received: 27 Apr 2026 | Revised: 09 Jun 2026 | Accepted: 02 Jul 2026 | Published: 29 Jul 2026

Abstract

This study develops and experimentally validates an interpretable machine learning framework integrating Extreme Gradient Boosting (XGBoost) with Shapley Additive Explanations (SHAP) to predict the 28-day compressive strength (CS) of fly ash-based geopolymer concrete (FA-GPC). A database of 355 experimental samples with 14 input parameters was compiled from literature. XGBoost outperformed support vector regression and decision tree models, achieving a test R2 of 0.930 with robustness confirmed by 10-fold cross-validation. SHAP analysis identified fly ash content, curing temperature, Al2O3 content in fly ash, and NaOH concentration as the most significant positive contributors, while water content showed a strong negative influence. Optimal thresholds for key mixture ratios, including Na2SiO3/NaOH (~2.5) and alkaline activator-to-fly ash ratio, were revealed. Twelve new FA-GPC mixtures were cast and tested, yielding excellent agreement between predictions and experiments (mean absolute error = 2.41 MPa). A graphical user interface was developed to facilitate practical adoption without programming expertise.

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Shi, X.; Hu, H.; Duan, Z.; Li, Y. Comparative and Interpretable Machine Learning for Fly Ash Geopolymer Strength: Prediction, Validation and Optimization. Durability and Sustainability 2026, 1 (1), 1.
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