# Estimation of the Ultimate Strength of FRP Strips-to-Masonry Substrates Bond
###### Abstract
[PERSON] et al. [10] examined the effect of elevated temperature on the mechanical properties of glass fiber-reinforced polymer. They showed a mass reduction in the shear modulus and compressive strength due to the impact of the elevated temperature (i.e., 200 \({}^{\circ}\)C). In addition, [PERSON] et al. [11] studied the effect of localized elevated temperatures on the static behavior of concrete members retrofitted with glass fiber-reinforced polymer. They showed that the value of reduction in the loading capacity depends on reinforcing geometry, especially in the overlapping area. The use of novel, innovative technologies and materials, e.g., FRP, involves the obligation to perform a lot of research and testing to confirm its effectiveness. It is also possible to use soft techniques in addition to research and tests. Nowadays, soft computing techniques were widely accepted to predict the behavior of engineering problems [38; 39]. For a unique issue, the accuracy of the results depends on the method selected for solving the problem. There are different well-known soft computing techniques. For example, [38] utilized the neuro-fuzzy and neural network techniques to forecast debonding behavior of FRP sheets in masonry elements. They concluded that an optimal adaptive neuro-fuzzy inference system predicted the bond strength of FRP-to-masonry elements by 39% and 23% in the form of root mean square errors and mean absolute errors, respectively, compared to the optimal multiple non-linear regression models. The genetic programming (GP) method was applied to assess the bond strength of the FRP-to-concrete composite joint [40]. Finally, an empirical formula based on the GP model was proposed by [PERSON] et al. [40], which showed an appropriate agreement with the experimental results. Researchers utilized different soft computing techniques to discover the existing behavior between the parameters in an engineering problem. For example, the parallel hyper-cubic gene expression programming method was used to measure slump flow [41]. Soft computing methods (i.e., stepwise regression, neural network, neuro-fuzzy, and GP) were adopted to estimate the strength improvement of concrete cylinders confined using FRP [42]. The neuro-fuzzy method, regression, and analytical models were also applied to offer a model for predicting the curvature and bending ductility factors for the high-performance concrete beams strengthened with CFRP sheets [43; 44]. The gene expression programming technique was adopted in [45] to estimate the thermal behavior of evacuated tube solar collectors. The Multiple-Kernel Support Vector Regression algorithm was used to estimate the water quality parameter [46]. Other authors applied the Mars Model to predict the water quality index [47]. The analytical models only use a specific dataset; therefore, they can predict engineering problems with lower accuracy [48]. [PERSON] et al. [48] proposed new models to predict the fiber-reinforced cementitious matrix to concrete bond utilizing robust soft models (i.e., curve-fitting, Gaussian Process Regression, and the Adaptive Neuro-Fuzzy Inference System). They also performed a sensitivity analysis to find the critical parameter in predicting the fiber-reinforced cementitious matrix to concrete bond [48]. Based on the published scientific background, it can be concluded that soft techniques may be a good solution to predict the existing relationship between the parameters in an engineering problem. Therefore, this paper utilizes four robust techniques (i.e., ANN, KFCV, Mars, and M5 model tree techniques) to forecast the ultimate strength of FRP-to-masonry bond. In fact, this paper uses four robust techniques to develop new formulae with higher precision than the existing examples. In this regard, we tried to decrease the computational cost by introducing new parameters to reduce the number of input parameters.