Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM
Descripción general
Resumen del artículo
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.
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Scientists taught a smart computer to guess how strong concrete beams are after they're fixed with a special fabric. They found the computer was really good at guessing, and learned that the steel inside, the beam's size, and the fabric's size were what made it strongest.
Posibles conflictos de intereses
The authors acknowledge funding from the Qatar National Research Fund and the Natural Sciences and Engineering Research Council of Canada. However, no specific conflicts related to FRCM manufacturers or commercial interests are identified.
Limitaciones identificadas
Explicación de la calificación
This paper presents a novel approach to predicting flexural capacity in FRCM-strengthened RC beams using machine learning. The use of xgBoost and SHAP analysis is commendable. While the reliance on a lab-generated dataset and the simplification of some input parameters are limitations, the study's innovative methodology and thorough comparative analysis warrant a strong rating. The identified funding sources do not raise significant conflict-of-interest concerns.
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