An Extreme Gradient Boosting approach to estimate the shear strength of FRP reinforced concrete beams

Although fiber-reinforced polymer (FRP) bars have gained much attention recently, their material characteristics, particularly the elastic modulus, are markedly different from conventional reinforcement. This explains why classical calculation models cannot correctly capture the behaviors of FRP-reinforced concrete (RC) beams in structural engineering. As a novel approach, this study aims to construct a machine learning (ML) model for predicting the shear strength (SS) of FRP-RC beams with and without stirrups. An Extreme Gradient Boosting (XGB) model is selected thanks to its effectiveness and robustness in tackling many complicated problems. An experimental database encompassing 453 examples is utilized, with input variables covering the FRP-RC beams' geometry, the mechanical properties of concrete, and two reinforcement components. The selection of hyperparameters of XGB model is first conducted, followed by a prediction performance assessment using common statistical measures. Additionally, this study conducts feature importance analysis and employs SHAP values technique to discover the inputs versus output relationship, as well as to identify the most significant features affecting the SS of FRP-RC beams. The outcome of this research, which lies in developing a reliable and accurate ML model that can handle both FRP-RC beams with and without stirrups, may favor the application of ML models to various real-world engineering problems.

Automated summary

This study develops a reliable and accurate machine learning (ML) model to predict the shear strength of fiber-reinforced polymer (FRP) reinforced concrete (RC) beams. Through feature importance analysis and data techniques, the most significant features affecting the shear strength of the beams are identified.