Shear strength prediction of FRP-strengthened concrete beams using interpretable machine learning

Abstract

This study identified the factors affecting the contribution of externally bonded fiber reinforced polymer (FRP) composite (Vf) to the shear strength of reinforced concrete (RC) beams with internal shear reinforcement through the use of interpretable machine learning (ML). A comprehensive database with 442 RC beams strengthened in shear with FRP was established and subjected to data anomaly detection using the isolation forest algorithm. Six ML models (artificial neural networks (ANN), XGBoost, random forest, CatBoost, LightGBM, AdaBoost algorithms) were trained to predict Vf. The ML models outperform six traditional explicit equations commonly used in design codes or the literature in terms of accuracy and variation. Based on the interpretable ML, the important variables were revealed; in particular the effective height of FRP, shear span ratio, and reinforcement method (U-wrap, full wrapping, side bonding) had significant influence on Vf. Finally, driven by results of the ML models, an explicit equation was derived to predict Vf, considering the physical meaning of the key influencing parameters. This study combines ML models and traditional physical models to achieve a novel, interpretable ML method to predict Vf.