###### Abstract  
The accurate prediction of reference points on the axial stress-axial strain relationship of fiber-reinforced polymer (FRP)-confined concrete is vital to pre-design structures made with this system. This study uses an artificial neural network (ANN) for predicting hoop rupture strain (\(\varepsilon_{h,np}\)) and transition zone, namely transition strain (\(\varepsilon_{c1}\)) and stress (\(\vec{f}_{c1}\)), on axial stress-strain curves of FRP-confined concrete. These are key parameters for estimating a transition zone of stress-strain curves. In this study, accompanied with these parameters, ultimate condition parameters, including compressive strength and ultimate axial strain, were predicted using a comprehensive database. Various combinations of input data and ANN parameters were used to increase the accuracy of the predictions. A sensitivity analysis and a model validation assessment were performed to evaluate the predictability of the developed models. At the end, a comparison between the proposed models in this study and existing ANN and design-oriented models was presented. It is shown that the accuracy of the developed ANN models in this study is higher or comparable to that of existing ANN models. Additionally, the developed models in this study to predict \(f_{c1}\) and \(\varepsilon_{c1}\) exhibit a higher accuracy compared to existing design-oriented models. These results indicate that the proposed ANN models capture the lateral confinement effect on ultimate and transition zones of FRP-confined concrete with a more robust performance compared to existing models. FRP-confined concrete; hoop rupture strain; artificial neural network (ANN); ultimate axial strain; transition zone; compressive strength 2023  
have been versatile models due to their ability in predicting the whole stress-strain curve. However, these models depend significantly on the concrete dilation behavior. Existing predictions for this behavior were either inaccurate or time-consuming due to the need for great computational effort [10; 11; 12; 13]. Assessment of existing design-oriented expressions expanded by different studies showed that they could approximately provide accurate predictions for compressive strength (\(f^{\prime}{}_{cc}\)) and axial transition stress (\(f^{\prime}{}_{c1}\)) [4; 9]. However, they could not accurately estimate the strain corresponding to \(f_{cc}\) (\(\epsilon_{cu}\)) [4; 9]. Furthermore, the existing expressions for \(\epsilon_{cu}\) prediction used experimental hoop rupture strain (\(\epsilon_{h,rup}\)). However, \(\epsilon_{h,rup}\) does not readily exist in designing procedures. Additionally, [PERSON] et al. [14; 15] discussed that the reported experimental \(\epsilon_{h,rup}\) had an inconsistency. In addition, some of the experimental data could not follow the trend of \(\epsilon_{h,rup}\). They discussed that this is partly due to location of shear planes and using strain gauges as a measurement method [3; 14; 15]. Similar to \(\epsilon_{cu}\) and \(\epsilon_{h,rup}\), accurate estimation of axial strain at the transition zone (\(\epsilon_{c1}\)), as another key reference strain, was not possible using existing proposed design-oriented expressions [4]. These observations in the performed literature review indicate that more investigation is essential on the prediction of significant reference strains, namely \(\epsilon_{cu}\), \(\epsilon_{h,rup}\), and \(\epsilon_{c1}\). Data-driven-based methods recently have received a great deal of attention due to their simplicity in use and usually offering a closed-form model for predictions. These models include various types of evaluation algorithms, including artificial neural network (ANN), generic programming (GP), stepwise regression, and fuzzy logic algorithms [16]. [PERSON] et al. [17] used a neural network as both a knowledge-based and a data-driven technique to predict the characteristics of different materials. Their prediction was developed directly from experimental datasets by ignoring prior assumption and human observations.