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<p>Shear strengthening of existing reinforced concrete beams via externally bonded fiber-reinforced polymer (FRP) systems is widely practiced; however, accurate prediction of shear capacity remains challenging due to complex interaction mechanisms, regime-dependent failure behavior, and the limited generalization of existing design-code equations. Code-based models calibrated on restricted experimental data often exhibit bias and large scatter across varying geometries, shear span-depth ratios, and FRP layouts, whereas purely data-driven machine-learning approaches typically lack physical interpretability. To address these limitations, a mechanism-aware physics-guided machine-learning framework is proposed for predicting the shear strength of FRP-strengthened reinforced concrete beams. The framework decomposes total shear resistance into a mechanics-based concrete contribution and a data-driven residual component modeled via XGBoost, enabling variance reduction and physically informed learning. A compiled database of 275 experimental beam tests is classified into low <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:\frac{a}{d}\le\:2.5\)</EquationSource> </InlineEquation> and high <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:\frac{a}{d}&gt;2.5\)</EquationSource> </InlineEquation> shear span-depth regimes to reflect distinct dominant failure mechanisms. In the high <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\:a/d\)</EquationSource> </InlineEquation> regime, the model achieves R² = 0.927, RMSE = 19.33 kN, and MAE = 14.58 kN, whereas in the low <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\:a/d\)</EquationSource> </InlineEquation> regime it attains R² = 0.771, RMSE = 72.67 kN, and MAE = 44.69 kN, consistent with greater mechanism variability. Explainable-AI analyses confirm regime-dependent parameter influence and saturation of FRP effectiveness, demonstrating mechanically coherent and interpretable predictions. Potential applications to reliability-based assessment and design-code calibration require additional uncertainty quantification and external validation beyond the scope of this study.</p>

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Mechanism-aware physics-guided XGBoost model for shear strength prediction of FRP-strengthened RC beams

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  • 出版模式：

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  • 发表日期：

    2026年4月19日

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  • Badhon Singha,
  • Md. Sadiqul Islam,
  • Nafis Niaz Chowdhury

摘要

Shear strengthening of existing reinforced concrete beams via externally bonded fiber-reinforced polymer (FRP) systems is widely practiced; however, accurate prediction of shear capacity remains challenging due to complex interaction mechanisms, regime-dependent failure behavior, and the limited generalization of existing design-code equations. Code-based models calibrated on restricted experimental data often exhibit bias and large scatter across varying geometries, shear span-depth ratios, and FRP layouts, whereas purely data-driven machine-learning approaches typically lack physical interpretability. To address these limitations, a mechanism-aware physics-guided machine-learning framework is proposed for predicting the shear strength of FRP-strengthened reinforced concrete beams. The framework decomposes total shear resistance into a mechanics-based concrete contribution and a data-driven residual component modeled via XGBoost, enabling variance reduction and physically informed learning. A compiled database of 275 experimental beam tests is classified into low \(\:\frac{a}{d}\le\:2.5\) and high \(\:\frac{a}{d}>2.5\) shear span-depth regimes to reflect distinct dominant failure mechanisms. In the high \(\:a/d\) regime, the model achieves R² = 0.927, RMSE = 19.33 kN, and MAE = 14.58 kN, whereas in the low \(\:a/d\) regime it attains R² = 0.771, RMSE = 72.67 kN, and MAE = 44.69 kN, consistent with greater mechanism variability. Explainable-AI analyses confirm regime-dependent parameter influence and saturation of FRP effectiveness, demonstrating mechanically coherent and interpretable predictions. Potential applications to reliability-based assessment and design-code calibration require additional uncertainty quantification and external validation beyond the scope of this study.