Gene-expression programming-based models for comprehensive evaluation of bond strength and critical slip of various types of FRP bars in concrete

This study aims to present a novel approach to assess the bond behavior of various types of FRP bars in concrete, based on an extensive dataset. The novelty of this research lies in evaluating both the bond strength and the critical slip, which forms the basis for creating a bond model between the bars and concrete. Furthermore, the model is constructed using advanced machine learning techniques, specifically gene-expression programming (GEP), and its reliability is ensured by relying on a more extensive and diverse experimental dataset compared to previous studies. To train and validate the GEP model, data were collected from experiments involving 793 test specimens, categorized into five groups based on surface treatments: smooth (Sm), sand-coated (SC), helically wrapped (HW), grooved (Gr), and a hybrid surface (Hbr) achieved by combining SC and HW. The model also considers a wide range of other parameters, including compressive strength of concrete and diameter, tensile strength, elastic modulus, and embedment length of FRP bars. The verification results using a separate testing dataset demonstrate that the proposed models accurately predict the bond strength and critical slip of FRP bars in concrete, achieving R2 values of 0.794 and 0.88, respectively. Comprehensive parametric analyses are performed to clarify the influence of key parameters on the bond and slip behavior of FRP bars in concrete. The analysis results confirm the critical role of the surface type and elastic modulus in enhancing bond strength; particularly, among the different surface treatment methods, the HW treatment exhibits the highest bond strength, followed by the HBr, Gr, and SC surface types.