Development of an artificial neural network based-prediction model for bond strength of FRP bars in concrete

Abstract

Fiber-reinforced polymer (FRP) bars have garnered increasing attention in recent years due to their superior corrosion resistance, offering a potential solution to the significant drawback of steel corrosion in concrete. For the widespread utilization of FRP bars in concrete structures, determining the bond strength between FRP bars and concrete is a crucial topic. This study seeks to develop a prediction model to estimate the bond strength of FRP bars in concrete, utilizing an extended dataset from 1010 pull-out tests. Initially, the study evaluates the applicability of several bond strength formulas from existing codes. Subsequently, two prediction models, namely a multivariate linear regression model and an artificial neural network (ANN) model, are introduced for estimating the bond strength of FRP bars in concrete. The results indicate that the correlation between the evaluation values of existing formulas and the experimental value is very low. This is because these formulas have not yet been updated to encompass the expanded usage scopes of FRP bars with various surface processing methods and types of concrete. While the multivariate linear regression model outperforms these formulas, its accuracy is still relatively low; in contrast, the ANN demonstrates superior performance, achieving an R^2 value for both the validation and test set of more than 0.92. The findings highlight that, when considering a broader range of applications, the ANN serves as a robust tool for accurately predicting the bond strength of FRP bars in concrete, in comparison to traditional formulas and linear regression models. This assessment approach provides engineers with a convenient, high-precision tool for designs utilizing various forms of FRP bars and diverse types of concrete in practical design scenarios