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<p>Large rupture strain fiber-reinforced polymer (LRS-FRP)-confined concretes are increasingly used in safety–critical infrastructure due to their high ductility and load-carrying capacity; however, accurate prediction of compressive strength (CS) in non-circular sections remains challenging due to non-uniform confinement induced by geometric irregularities, which limits the reliability of existing empirical models and design codes developed mainly for circular sections. To address this limitation, this study develops a reliability-oriented, data-driven framework that combines Bayesian-optimized ensemble machine learning, model interpretability, and uncertainty quantification. Six algorithms including random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost), histogram based gradient boosting (HistGBM), light gradient boosting machine (LightGBM) and categorical boosting (CatBoost) were trained using an experimental database of 174 non-circular LRS-FRP-confined concrete specimens. Model interpretability was achieved using Shapley additive explanations (SHAP), while predictive reliability was systematically evaluated through uncertainty-aware performance assessment. All models demonstrated strong generalization, with testing coefficients of determination (R<sup>2</sup>) ranging from approximately 0.96–0.99, and boosting-based methods consistently outperforming bagging approaches. CatBoost (testing R<sup>2 </sup>≈ 0.985) exhibited the best overall performance, the lowest prediction errors, and the most reliable uncertainty estimates. Accordingly, the overall performance ranking was identified as CatBoost &gt; HistGBM &gt; XGBoost &gt; ERT &gt; LightGBM &gt; RF. The results clearly indicate that high predictive accuracy alone is insufficient for reliable modeling of non-circular LRS-FRP-confined concrete and that uncertainty-aware evaluation is essential. SHAP-based analysis yielded physically consistent insights, identifying LRS-FRP thickness, unconfined concrete strength, and section corner radius as the dominant contributors to CS, while highlighting the critical role of post-transition LRS-FRP stiffness in sustaining effective confinement. Overall, the proposed framework offers an interpretable and reliability-aware alternative to conventional models and provides a robust predictive tool for engineering design and assessment of non-circular LRS-FRP-confined concrete.</p>

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Bayesian-optimized explainable boosting models for compressive strength of non-circular LRS-FRP-confined concrete

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月5日

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  • Naser Safaeian Hamzehkolaei,
  • Yaser Moodi,
  • Jafar Jafari-Asl

摘要

Large rupture strain fiber-reinforced polymer (LRS-FRP)-confined concretes are increasingly used in safety–critical infrastructure due to their high ductility and load-carrying capacity; however, accurate prediction of compressive strength (CS) in non-circular sections remains challenging due to non-uniform confinement induced by geometric irregularities, which limits the reliability of existing empirical models and design codes developed mainly for circular sections. To address this limitation, this study develops a reliability-oriented, data-driven framework that combines Bayesian-optimized ensemble machine learning, model interpretability, and uncertainty quantification. Six algorithms including random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost), histogram based gradient boosting (HistGBM), light gradient boosting machine (LightGBM) and categorical boosting (CatBoost) were trained using an experimental database of 174 non-circular LRS-FRP-confined concrete specimens. Model interpretability was achieved using Shapley additive explanations (SHAP), while predictive reliability was systematically evaluated through uncertainty-aware performance assessment. All models demonstrated strong generalization, with testing coefficients of determination (R2) ranging from approximately 0.96–0.99, and boosting-based methods consistently outperforming bagging approaches. CatBoost (testing R2 ≈ 0.985) exhibited the best overall performance, the lowest prediction errors, and the most reliable uncertainty estimates. Accordingly, the overall performance ranking was identified as CatBoost > HistGBM > XGBoost > ERT > LightGBM > RF. The results clearly indicate that high predictive accuracy alone is insufficient for reliable modeling of non-circular LRS-FRP-confined concrete and that uncertainty-aware evaluation is essential. SHAP-based analysis yielded physically consistent insights, identifying LRS-FRP thickness, unconfined concrete strength, and section corner radius as the dominant contributors to CS, while highlighting the critical role of post-transition LRS-FRP stiffness in sustaining effective confinement. Overall, the proposed framework offers an interpretable and reliability-aware alternative to conventional models and provides a robust predictive tool for engineering design and assessment of non-circular LRS-FRP-confined concrete.