and structures [1]. Splice methods and their effect on RC structures' behavior has been extensively researched experimentally and numerically [2; 3; 4; 5; 6; 7], considering their remarkable importance. Regarding splice methods, they can be generally categorized into three main classifications: lap, mechanical, and welded splices. Lap splices are recognized as the most common and inexpensive method applied for connecting rebars due to its simplicity [8]. However, its shortcomings (e.g., bar congestion), which lead to poor performance of structures, have motivated researchers to propose alternative splice approaches [9; 10; 11]. The application of welded splices is also scarce as it requires specific operation skills to achieve a reliable splice. Moreover, the lack of investigations for conclusions and code provisions on welded splices prevents them from being widely used in construction [9; 12]. Mechanical splices, therefore, are claimed to be an appropriate splice method because their implementation (i) is quick and easy, (ii) does not require special skills, (iii) reduces bar congestion, and (iv) leads to better performance compared to other methods [9; 10]. Mechanical splices could be categorized into five main groups, namely, threaded (the most common type), grouted, swaged, bolted, and headed bar couplers [6; 13]. A combination of the above-mentioned methods (e.g., threaded-screw couplers) has also been proposed by researchers [14; 15; 16; 17; 18]. The stress transformation is different in each method. As an example, in the threaded, swaged, bolted, and headed bar couplers, force is transferred through thread locks, grout, interlocks between the bar rips and swaged sleeve, friction between bolts and bar rips, and the male-female elements, respectively [9]. In all the mechanical methods, the rebars are connected by a sleeve and, therefore, the length and diameter (or thickness) of the sleeve affects the splice performance greatly. It is claimed that longer couplers could decrease RC element ductility and deformation capacity [12; 18; 19] and cause earlier failure due to a larger concrete-bar slip [19]. The coupler rigidity is increased by increasing its outer diameter (or thickness), which might decrease ductility [13; 19]. The other remarkable parameter which can affect the performance of either non-spliced or spliced bars is temperature [20; 21]. [PERSON] and [PERSON] concluded that increasing temperature up to 400 \({}^{\circ}\)C does not notably affect stiffness and strength, whereas yield plateau becomes less visible for temperatures higher than 300 \({}^{\circ}\)C. Furthermore, the ultimate strains of both spliced and non-spliced bars are reduced with increasing temperatures by more than 7.5% of the ultimate strain at ambient temperature [20]. Due to the significant influence of the above-mentioned parameters on the mechanical properties ofnon-spliced and spliced bars, their stress-strain behavior is determined under tensile testing according to design codes (e.g., ASTM [22]). The test is time-consuming and expensive, particularly when an appropriate coupler geometry with a reliable performance needs to be achieved. As a result, machine learning (ML)-based techniques have been developed by researchers as a reliable, quick, and inexpensive alternative approach for experimental tests [23; 24; 25; 26; 27]. The limited ML-based models presented for predicting the mechanical properties of non-spliced and spliced bars [28; 29] have proven the accuracy of these ML-based techniques. As an example, [PERSON] et al., predicted the ultimate tensile strength of both non-spliced and spliced bars using nonlinear regression and ridge regression, as well as incorporating an Artificial Neural Network (ANN). In their study, the proposed nonlinear regression method resulted in more accurate values compared to other techniques [28].