###### Abstract
They discussed that in the analysis of neural network models, the characteristics of materials can be implicitly captured using parameters of weight [17]. [PERSON] et al. [18] developed hybrid ANN models for durability analysis of glass-FRP rebars in an alkaline concrete environment. [PERSON] et al. [19] predicted the compressive strength of concrete using an ANN mesoscale concretization model. They found that their model could accurately capture the mechanical response of the concrete. [PERSON] et al. [20] developed a back-propagation ANN model to capture the interface bond behavior of FRP-reinforced concrete. Different meta-heuristic algorithms (e.g., particle swarm optimization, social spider optimization) were used by [PERSON] et al. [21] to improve the prediction accuracy of a gradient boosting-based method for estimating the resilient modulus of flexible pavement foundations. Finally, [PERSON] and [PERSON] [22] used an artificial bee colony algorithm to design multilayer microwave absorbing foam concrete. In the case of FRP-confined concrete, [PERSON] et al. [1] and [PERSON] et al. [16] reported that numerous predictions were developed using data-driven approaches to estimate the ultimate condition of the confined concrete having a circular-shape cross-section. [PERSON] et al. [1] reported the existence of more than 88 expressions to predict \(\epsilon_{cu}\) and \(f_{cc}\) of confined concretes with FRP. Different studies used ANN and GP to estimate \(\epsilon_{cu}\) and \(f_{cc}\) (e.g., [16; 23; 24; 25; 26; 27]). It was reported that an ANN was able to predict \(f_{cc}\) more accurately compared to traditional approaches [16]. [PERSON] et al. [28] used the other type of neural network, i.e., radial basis function network, to predict \(f^{\prime}{}_{cc}\). [PERSON] et al. [29] determined \(f_{cc}\) by predicting the efficiency of a FRP jacket using an ANN technique. Recently, [PERSON] et al. [30] used ANN to build a confinement model to predict the ultimate condition of FRP-confined concrete. However, their model was limited to the test results obtained in their study. [PERSON] et al. [16] predicted \(\epsilon_{cu}\) in addition to \(f_{cc}\) using an ANN accompanied with the prediction of the stress-strain relationship. Similar to the concretes with a circular cross-section, the ultimate condition of the concretes with square and rectangular cross-sections were also examined by ANN analysis in different studies [31; 32; 33]. Nonetheless, the literature review revealed that no research has examined the prediction of a transition zone using an ANN. Existing models were either complex, dealt with a small number of databases, or ignored key influential input parameters on the behavior of FRP-confined concrete. The availability of the five key reference parameters (i.e., \(\varepsilon_{cu}\),\(f_{cc}\), \(\varepsilon_{c1}\),\(f_{c1}\), and \(\varepsilon_{h,rup}\)) helps to model the whole curve of the FRP-confined concrete accurately, as was explained by [PERSON] et al. [4]. An ANN can handle a complex database with large variables, identify sensitivity of input and output parameters, and establish relations between the input and output variables. This study, as the first study in the literature, used an ANN technique to predict the transition zone on the stress-strain relationship and hoop rupture strain of FRP-confined concrete. The accuracy of the ultimate point prediction on the curve was also examined. A comprehensive database collected by this research group was used. Firstly, a brief introduction of the database and the principals of an ANN were provided. Afterward, the influence of an input variable in an ANN to predict different critical points on the curves was examined. A sensitivity analysis was performed for examining the influence of input variables on the accuracy of the final selected ANN option. This was accompanied by model validation using different statistical indicators. At the end, the accuracy of the developed models by ANN was compared to that of existing models.