Abstract
The bond between rebar and concrete is one of the significant parameters which impacts the behavior of reinforced concrete (RC) elements. Considering the corrosion of steel bars and the widespread use of glass fiber-reinforced polymer (GFRP) bars, it is necessary to study the bond behavior between concrete and GFRP rebars. Pullout testing is one of the most common and convenient methods to evaluate the bond strength between rebar and concrete. It is shown that the existing models have not been able to accurately estimate the bond strength between GFRP rebars and concrete. A database containing 324 GFRP-reinforced pullout specimens with splitting and pullout failure modes has been collected. To predict the bond strength, some soft computing methods such as single-layer perceptron neural network (SLPNN), two-layer perceptron neural network (TLPNN), radial basis function neural network (RBFNN), kriging, multivariate adaptive regression splines (MARS), and M5 tree models are utilized. K-fold cross-validation and optimization of tuning parameters for soft computing methods have been used to increase the precision, efficiency, and generality of the proposed models. Moreover, a nonlinear regression method is used to propose practical equations for predicting the GFRP bar–concrete bond strength. Compared to the best existing models, the proposed relations have improved the correlation coefficient by about 69 and 9% for the pullout and splitting failure modes, respectively. The proposed soft computing models have outperformed the other models by average correlation coefficients of 0.96 and 0.97 for samples with pullout and splitting failure types, respectively.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Farahi Shahri, S., Mousavi, S.R. Predicting the Bond Strength Between Concrete and Glass Fiber-Reinforced Polymer Bars Using Soft Computing Models. Iran J Sci Technol Trans Civ Eng 47, 3507–3522 (2023). https://doi.org/10.1007/s40996-023-01125-7
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DOI: https://doi.org/10.1007/s40996-023-01125-7