Estimation of the Bond Strength of Fiber-Reinforced Polymer Bars in Concrete Using Artificial Intelligence Systems

Fiber-reinforced polymer (FRP) bars have recently been introduced to the market as an alternative to steel for internal reinforcement for concrete construction exposed to situations that could cause corrosion. The bond behavior of FRP bars varies from that of steel bars, mostly due to variations in material properties and surface textures. Because of the unexpected nature of the crucial FRP–concrete interfacial (FCI) bond strength, the bond strength between FRP bars and concrete cannot be exactly determined. Numerous experimental investigations have been conducted with related empirical models established in an attempt to resolve this problem. These models were found to have a restricted capacity for generalization due to the small sample sizes of the experiments. Therefore, a more powerful numerical technique capable of processing large data sets with all possible parameters that may affect the relationship and considering the nonlinearity of data tendency is needed. In this study, the artificial neural networks technique and adaptive neuro-fuzzy inference system were utilized to predict the FRP–concrete bond behavior based on 238 data points collected from different studies in the literature. The performance of the ANN and ANFIS models in predicting the bonding strength was compared to other models published in the literature and codes. The results showed that the ANN and ANFIS models gave higher prediction performance than other models, with a slight advantage for the ANN model. For instance, the R-squared values of the proposed ANN and ANFIS were 0.94 and 0.92, respectively, for 20 data points that were not used to develop the ANN and ANFIS models. Based on the sensitivity analysis, the FRP diameter and compressive strength of concrete were found to be the most effective parameters on the bond strength in both the ANN and ANFIS models. In contrast, the bar position and surface texture had a lower importance index.

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