Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM
This study uses machine learning, specifically the xgBoost model, to predict the flexural capacity of reinforced concrete beams strengthened with fabric-reinforced cementitious matrix (FRCM). The xgBoost model outperformed existing analytical models and a SHAP analysis identified the area of tensile steel reinforcement, width of the beam, FRCM area, and effective beam depth as the most influential factors.