An empirical model for predicting time-dependent strength recovery of fire-damaged concrete under post-fire curing regimes: a statistical and machine learning approach

Abstract

Post-fire curing techniques for thermally damaged concrete are widely discussed, yet no dedicated study has quantified the extent of strength recovery. This research introduces an empirical model built upon a comprehensive experimental database that accounts for key input variables: exposure temperature, re-curing method, re-curing duration, and water-to-binder (W/B) ratio. The model was developed using 464 collected datasets. Statistical evaluation through ANOVA and regression analysis identified temperature as the dominant factor influencing strength recovery (F-value = 109.37), followed by re-curing type, re-curing duration, and W/B ratio. An optimization analysis further determined the conditions that maximize strength recovery: temperatures of 150–250 °C, hybrid re-curing methods, re-curing periods of 94–182 days, and W/B ratios between 0.30 and 0.45. The proposed model predicts the strength recovery percentage with demonstrable accuracy. It was subsequently refined into a more conservative formulation suitable for design codes, incorporating a conservatism parameter of 53.2%. Among the regression-based machine learning algorithms tested, XGBoost achieved the highest predictive performance (R² = 0.74, MAE = 8.81%).

Keywords

Concrete / Compressive strength / Post-fire curing / Strength recovery / Machine learning

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