Abstract
One of the principal parameters which affect the behavior of reinforced concrete (RC) elements is the bond of the bar to concrete. Considering the corrosion of steel bars and the widespread use of glass fiber reinforced polymer (GFRP) bars, it is necessary to study the bond between concrete and GFRP rebars. The hinged beam is one of the tests which simulates the relatively actual stress of RC members. In this study, a database containing 116 GFRP reinforced hinged beams with pull-out failure mode has been collected. To predict the bond resistance, Kriging, multivariate adaptive regression splines (MARS), and nonlinear regression techniques are utilized. K-fold cross-validation, sensitivity analysis of input variables, and optimization of tuning parameters for soft computing methods have been used to increase the precision, efficiency, and generality of the proposed models. Also, using the nonlinear regression method, a practical and straightforward relationship is suggested to forecast the bond resistance. The proposed Kriging, MARS, and nonlinear regression models outperformed the other models with determination coefficients of 0.98, 0.96, and 0.78, respectively. Parametric analysis of the variables has also been performed to investigate the influence of various input parameters on the bond resistance of GFRP bars in hinged beams.
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Shahri, S.F., Mousavi, S.R. Predicting the Bond Resistance of Glass Fiber Bars in Hinged Beams Employing Enhanced Soft Computing Techniques. KSCE J Civ Eng 27, 3901–3911 (2023). https://doi.org/10.1007/s12205-023-0197-7
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DOI: https://doi.org/10.1007/s12205-023-0197-7