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Machine learning based prediction of joint shear strength for fiber reinforced concrete beam-column connections
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DOI：10.1016/j.compstruct.2026.120772.png)
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Abstract
En 中文
Beam-column joints are among the most susceptible regions of reinforced concrete (RC) moment resisting frames, prone to high shear forces. Fiber reinforced concrete (FRC) mitigates this susceptibility by preventing brittle joint shear failure and promoting beam plastic hinging. Therefore, accurately predicting FRC beam-column joint shear strength is essential for reliable seismic performance assessment. However, existing equations were calibrated on limited datasets mostly containing only one fiber and joint type, evaluated on the same data used for development, overlooking nonlinear feature interactions. Machine learning (ML) offers a data-driven alternative capturing these complex dependencies, handling categorical inputs, and objectively evaluating accuracy on unseen data. For RC joints, ML models are proved to outperform analytical formulations, confirming their suitability for shear strength prediction. This study extends that capability to FRC joints by developing a comprehensive framework with ten ML models, built on a database of 220 specimens. XGB and other ensemble methods achieve R2 above 0.90 and MAPE below 9 % for test set, validated across 100 repeated random splits. SHAP identifies compressive strength as the most influential feature. A user-friendly GUI is developed to enable practical application of models, which provides a robust foundation for ML assisted seismic design of FRC structures.
Keywords:
Beam-column joint
Fiber reinforced concrete
Joint shear strength
Machine learning
SHAP
Graphical user interface
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7.1
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1.8W
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