Bond Strength Prediction Model of the Near-surface-mounted Fiber-reinforced Polymer Concrete Based on Gene Expression Programming

Fiber-reinforced polymer (FRP) has been widely used in concrete reinforcement projects. The bonding performance between the FRP and concrete influences the reinforcement effect well. Numerous studies were conducted on the bond strength of FRP externally bonded concrete, and a lot of empirical models were developed, while there were few empirical models of the bond strength of FRP near-surface-mounted (NSM) concrete. In order to accurately predict the bond strength between near-surface-mounted FRP and concrete, the gene expression programming (GEP) method was employed to develop a bond strength prediction model of NSM FRP bonded to concrete, and a specific calculation formula was established. The model was developed using six parameters including the concrete compressive strength, bond length, groove depth-to-width ratio, FRP axial rigidity, FRP tensile strength and epoxy tensile strength. The predicted values calculated by the proposed formula agreed well with the experimental values, indicating that the model was reliable. Through the sensitivity analysis of the model, it was found that the GEP model can reflect the internal relationship between bond strength and single factor, i.e., the bond strength increased with the increase of the bond length, concrete compressive strength, the groove depth-to-width ratio and FRP axial rigidity. The GEP model was compared with the empirical model and the wavelet neural network model. Six statistical indicators were selected to evaluate the prediction performance of these models. It can be found that the accuracy of the GEP model and the wavelet neural network model was high, and the errors were low where the coefficients of determination were 0.793 and 0.787, respectively. In general, the accuracy of the GEP model was slightly better than the wavelet neural network model, and the accuracy of the two models was much higher than the empirical models. Copyright ©2021 Advanced Engineering Sciences. All rights reserved.