## 1 Introduction  
In recent years, the strengthening of existing concrete structures using externally bonded composite sheets of fiber reinforced polymer (FRP) has gained significant popularity. One common technique is wrapping unidirectional FRPs around the circumference of a concrete column to increase its axial strength and ductility. It is well-known that a concrete core expands laterally under uniaxial compression, but such expansion is confined by the FRP. Therefore, the core is subjected to a three-dimensional compressive state of stress in which the performance of the concrete core is significantly influenced by the confining pressure [1; 2; 3; 4; 5].  
Many researchers studied the behavior of FRP-confined concrete and proposed a variety of confinement models for the ultimate condition of confined concrete under uniaxial compression loadings [6; 7]. The majority of FRP confinement models are design-oriented and were developed using a regression analysis [8; 9; 10; 11; 12]. There have also been several analysis-oriented models developed based on the mechanics of confinement and strain compatibility between concrete and the FRPwrap [13; 14; 15]. Recently, a new category of models has been proposed based on soft computing methods, such as artificial neural networks, generic algorithms, and fuzzy logic. Models in this category can handle complex databases containing a large number of independent variables, identify the sensitivity of input parameters, and provide mathematical solutions between dependent and independent variables [16]. [PERSON] and [PERSON] [16] proposed the utilization of neural networks to compute the strain and compressive strength of FRP-confined columns, and the results show agreement between proposed neural network models and experimental data. Also, there are several studies related to design-oriented and analysis-oriented models [9; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29].  
[PERSON] et al. [30] proposed a new model for evaluating the ultimate condition of FRP-confined concrete using genetic programming (GP). The model was the first to establish the ultimate axial strain and hoop rupture strain expressions for FRP-confined concrete on the basis of evolutionary algorithms. The results showed that the predictions from the suggested model aligned with a database compiled by the authors. The proposed models provided improved predictions compared to the existing artificial intelligence models. The model proved that more accurate results can be achieved in explaining and formulating the ultimate condition of FRP-confined concrete. The model assessment presented in that study clearly illustrated the importance of the size of the test databases and the selected test parameters used in the development of artificial intelligence models on their overall performance.  
This paper studies the capability of four soft computing techniques for predicting the ultimate strength and strain of FRP-confined concrete cylindrical specimens. The computing techniques include radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM), and M5 model tree (M5 Tree).