## 2 Methodology and Data Collection  
The main objective of this study was to present and evaluate models using machine learning for determining the ultimate strain of non-spliced and spliced steel reinforcement bars. To achieve this research objective, 225 data sets were collected from the literature available in peer-reviewed publications [18; 20; 34; 35; 36; 37; 38; 39; 40]. The collected database was then randomly divided into two sub-databases: (a) a training database for training the relationship between inputs and output, and (b) a testing database for assessing the accuracy of the models. In this study, 85% of the database was incorporated into the training database while 15% was used for testing. As reviewed and discussed in the first section, the parameters with the highest impact on ultimate strain--namely, the bar diameter (mm), steel grade, coupler geometry (length and outer diameter, mm), and temperature (\({}^{\circ}\)C)--were considered as input parameters to estimate the only output, ultimate strain. To determine the degree to which the input data affected the target output, Pearson correlation coefficients were determined and are given in Table 1. It is worth explaining that Pearson Coefficients are defined as the ratio of covariance of two parameters to the products of their standard deviation; \(\rho=Cov(X,Y)/\sigma_{x}\sigma_{y}\), and denotes a linear relationship between two variables. Unless otherwise mentioned, \(\rho\approx 1\) refers to a high linear relationship while \(\rho\approx 0\) stands for independence between factors; negative coefficients show a reverse relationship between two parameters. To compare the effect of input variables on ultimate strain more precisely, the Pearson coefficients are illustrated in a bar chart shown in Figure 1.  
The values given in Table 1 and the bar chart demonstrated in Figure 1 reveal that coupler geometry and temperature had a higher effect on the ultimate strain of non-spliced and spliced bars compared to the other input variables. Additionally, all the quantitative inputs (i.e., bar diameter, length of coupler, and outer diameter, along with the temperature) had an inverse relationship with ultimate strain. Regarding data collection, a comprehensive database was collected including various splice methods used for different bar sizes with different steel grades over a large range of temperature variation. A total of 134 out of 225 datasets are outcomes of a tensile test on non-spliced steel bars while the other 91 datasets are the mechanical properties of spliced bars with different methods. In terms of splice techniques, five mechanical techniques--namely, headed bars, shear-screw, grouted, taper threaded, swaged and threaded couplers (as displayed in Figure 2)--with different lengths and thicknesses were taken into consideration. It should be clarified that according to the design code (i.e., ACI 318-19: 18.2.7.1 [41]), regardless of the splice method, bars spliced by mechanical approaches should provide 1.25\(f_{y}\) of a non-spliced bar in both tension and compression. Although the mechanical splices in the database are different, they all meet the above-mentioned provision and therefore could be utilized in the database for the prediction models. It is noteworthy that this study focused on mechanical couplers and that neither lap nor welded splices (e.g., gas pressure welding, head-to-head) are included in the collected database. Table 2 provides the statistical properties of the other quantitative input and output parameters. Bars with diameters of 12-32 mm were included in the database since they are considered by designers and researchers in typical structures more than other sizes. Coupler geometry (i.e., length and outer diameter), as depicted in Figure 3, were in the range of 45.00-490.34 mm and 7.29-64.00 mm, respectively, which are the dimensions leading to the acceptable performance of couplers. Eventually, temperatures in the range of 20-600 \({}^{\circ}\)C, which were studied in the literature, were also considered in the database.