## 6 Summary and Conclusions  
The mechanical properties (e.g., ultimate strength and stain) of reinforcement bars (either spliced or non-spliced) must be determined before they are implemented in construction. These tests are sometime time-consuming and costly, particularly when an appropriate splice geometry needs to be obtained. As a result, this study aimed at developing ML-based models--namely Random Forest and Decision Trees--for the estimation of the ultimate strain of reinforcement steel bars. To this end, a database containing 225 datasets was collected and divided into training (85%) and testing (15%) databases. The parameters--namely, bar diameter, steel grade, splice method, length and outer diameter of the coupler, and temperature--which are claimed as the most effective factors on ultimate strain were defined as input variables for predicting ultimate strain. The predicted values were compared to the actual values and the accuracy of the proposed DT and RF models were assessed using performance metrics and a Taylor diagram. * The results proved the high ability of both DT and RF models for learning the relationship between input variables and the output. The quick and simple ML-based models proposed in this study, therefore, could be considered as a reliable alternative approach for time-consuming and costly experimental tests. * Both the DT and RF model exhibited highly reliable results, with R\({}^{2}\) scores higher than 85%. The DT model, however, illustrated higher accuracy (R\({}^{2}\) = 89%) compared to the RF model (R\({}^{2}\) = 85%). It should be explained that although RF generally leads to more reliable results, in this study, the predicted values obtained by the DT model were more accurate, because the training and testing datasets for each model were selected randomly in order to avoid any human effect on the prediction process. In other words, the training and testing databases were not the same, which caused the slight difference between the models' accuracy. In order to compare RF and DT more precisely, the same training and testing datasets should be chosen. This study, however, focused on the reliability assessment of Decision Tree and Random Forest models for estimating the ultimate strain of spliced and non-spliced steel bars, and the results proved their acceptable accuracy. * It is also noteworthy that comparing the accuracy of the models, which estimates the ultimate strain of bars, for each splice method could be conducted in further studies. To this end, (a) an extensive database including the results of tests on each splice type should be collected; (b) input variables for each method should be specified--as an example, the strength of grout could be considered as one of the inputs for models estimating the mechanical properties of grouted spliced bars; and (c) the databases should be large enough for training the relationships between inputs (which may be different for each splice technique) and output appropriately. Figure 8: Comparing the accuracy of the proposed models through a Taylor diagram. * The proposed models in this research could be used in a more generalized model for predicting the stress-strain behavior of spliced and non-spliced bars. More clearly, by combining models which predict other parameters (e.g., yield strength and strain) with the models proposed in this study, the stress-strain curve could be estimated without doing experimental tests. Conceptualization, V.F. and M.K. ; Data curation, H.D., M.J.M. and M.Z. ; Formal analysis, H.D., V.F., M.J.M. and M.Z. ; Funding acquisition, V.F. ; Investigation, H.D. and V.F. ; Methodology, H.D. and V.F. ; Project administration, M.Z. and M.K. ; Software, H.D., M.J.M. and M.Z. ; Supervision, V.F. ; Validation, M.J.M. and M.K. ; Writing--original draft, H.D. and V.F. ; Writing--review & editing, H.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.