Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches

This paper proposes application of neuro fuzzy and neural network for predicting debonding strength of retrofitted masonry elements. In order to achieve high-fidelity model, this study uses extensive experimental databases for bond test results between Fiber Reinforced Polymer (FRP) and masonry elements by collecting existing bond test subassemblage tests from the literature. Various influential parameters that affect debonding resistance including thickness of the FRP strip, width of the FRP strip, elastics modulus of the FRP, bonded length, tensile strength of the masonry block and width of the masonry block are considered as input parameters to the artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). Test results of the ANN and ANFIS models were compared with multiple nonlinear regression, multiple linear regression and existing bond strength models. The accuracy of the optimal MNLR model was increased by 39% and 23% with respect to RMSE and MAE criteria using ANFIS. The comparison results indicated that the ANN and ANFIS models performed better than the other models and could be successfully used for prediction of debonding strength of retrofitted masonry elements.