# A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders
## 1 Introduction  
A combination of high-strength fibers and matrix leads to the construction of a fiber-reinforced polymer (FRP). The primary role of the matrix is to bind these fibers together to construct structural shapes. Four common types of fibers (i.e., aramid, carbon, glass, and high-strength steel) and also two standard matrices exist (i.e., epoxies and esters) [1; 2]. A new area has been opened in the civil engineering field due to the beneficial properties of FRP in the repair and rehabilitation of existing structures. The FRP can create a continuous confinement action for the concrete member, and can also increase the corrosion resistance of members [3]. Hereby, FRPs are popularly used to repair or retrofit the reinforcing frame members [4; 5; 6; 7; 8; 9; 10]. Studies on the behavior of FRP and FRP-confined concrete have advanced rapidly in recent years [11]. There are a lot of publications proposing a formulafor FRP-confined concrete [12; 13; 14; 15; 16; 17; 18; 19]. These proposed formulas are usually based on the [PERSON] et al. method [20]. Nowadays, the use of artificial neural networks, Bayesian networks and neuro-fuzzy systems has a special place in engineering solutions, including FRP-strengthened concrete structures, structural optimization, water resource management, vibration control, bridge engineering, etc. [11; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. In the study of vibrations in buildings, e.g., caused by earthquakes, the search for alternative solutions is also underway (see [38; 39; 40; 41], for example). An artificial neural network (ANN) was used by [PERSON] and [PERSON] [42] to estimate the shear strength of FRP-reinforced concrete flexural members. [PERSON] et al. [43] used ANN, adaptive neuro-fuzzy inference system (ANFIS) and regression analysis to predict the compressive strength of no-slump concrete. [PERSON] and [PERSON] [44] predicted the shear strength of reinforced concrete deep beams using evolutionary multivariate adaptive regression splines. In addition, the M5 model tree, used by [PERSON] et al. [45], is capable of predicting the elastic modulus of recycled aggregate concrete. [PERSON] and [PERSON] [46] proposed a new formulation to estimate the curvature ductility factor for FRP-reinforced high-strength concrete beams using ANFIS and multiple regression methods. The ANFIS model was also used by [PERSON] and [PERSON] [47] to control a multi-degree of freedom structures equipped with an MR damper. The ANFIS and ANN models were applied by [PERSON] and [PERSON] [48] to compare results obtained for the shear strength of reinforced concrete beams with building codes. The strength of FRP connections using the backpropagation neural network was studied by [PERSON] et al. [49]. The deflection of high-strength self-compacting concrete deep beams was studied by [PERSON] et al. applying ANFIS [50]. [PERSON] and [PERSON] [51] used the genetic algorithm to predict the shear capacity of reinforced concrete beams reinforced with FRP sheets. Currently, seawater and sea sand concrete is also becoming popular due to the shortage of resources and, therefore, many researchers have focused their studies on these types of materials [52; 53; 54]. Some mechanical properties of FRP-confined concrete columns made of sea sand and seawater were studied by [PERSON] et al. [52]. They presented some theoretical models for hoop stress and strain relations and axial compression-strain relations. [PERSON] et al. [54] experimentally considered the effects of a chloride environment on the mechanical performance and durability of FRP-confined concrete columns made of seawater. In this paper, the feed-forward backpropagation neural network (FFBPNN) method has been used to estimate the ultimate compressive capacity of FRP-confined concrete cylinders. For this purpose, a set of previously published and available experimental data (281 instances) for concrete made of ordinary sand has been collected for training and testing.