## 3 Results
### SHAP Interpretation of the ML Models  
The SHAP summary plot presented in Figure 10 indicates that the predicted value of the confined concrete ultimate strength is most significantly influenced by the unconfined concrete ultimate strength, followed by the total thickness of the AFRP jacket and the length of the specimen. On the other hand, the elasticity modulus of the AFRP has the least impact on the model output. Since the dots representing \(\mathrm{f_{co}}\) have red colors to the right-hand side of the diagram, increasing the value of \(\mathrm{f_{co}}\) also increases the impact of \(\mathrm{f_{co}}\). A similar relationship also exists between \(\mathrm{t_{f}}\) and \(\mathrm{f_{cc}}\). However, the opposite is true for the length and diameter of the specimen, such that increasing these two quantities has a decreasing effect on the predicted \(\mathrm{f_{cc}}\) values. The feature dependence plots in Figure 11 further elaborate on the change in the model predictions, with regard to the four factors that have the largest effect, as demonstrated by the SHAP summary plot. Each feature dependence plot in Figure 11 contains information about two input features. The feature represented with the color bar to the right-hand side of the plot is the input feature most dependent on the feature represented on the horizontal axis. The values of the dependent feature affect the colors of the dots in Figure 11, and each one of these dots represents one of the samples from the dataset. Figure 11 illustrates how the SHAP value of \(\mathrm{f_{co}}\) rises in tandem with the value of \(\mathrm{f_{co}}\). For values of \(\mathrm{f_{co}}\) less than 80 MPa, the addition of \(\mathrm{f_{co}}\) into the model has a decreasing effect on the model output, whereas, for values of \(\mathrm{f_{co}}\) greater than 80 MPa, the addition of \(\mathrm{f_{co}}\) increases the model predictions. Similar trends can also be observed for \(\mathrm{E_{f}}\) and \(\mathrm{f_{f}}\), whereas the opposite trend can be observed for the specimen length L. For values of L less than 300 mm, the addition of this parameter into the model increases the predictions, whereas, for L values greater than 300 mm, the length of the specimen has a decreasing effect on the model outputs. A SHAP summary plot is shown in Figure 12 for the CatBoost model, which predicts the confined ultimate strain. The ultimate tensile strength of AFRP (\(\mathrm{f_{f}}\)) was found to have the greatest impact on the model predictions, followed by the unconfined ultimate strength and the AFRP jacket thickness. It was observed that the specimens' diameter had the least influence on the model predictions. Figure 13 shows the feature dependence plots for the four parameters with the greatest impact, according to Figure 12. According to Figure 13a, the increase in the value of \(\mathrm{f_{f}}\) also increases the SHAP value of this feature. Particularly at values greater than 2500 MPa, \(\mathrm{f_{f}}\) has an increasing effect on the model output. A similar trend can also be observed between \(\mathrm{t_{f}}\) and its impact on the model output in Figure 13d. Up to a thickness of 0.3 mm, the confining jacket thickness has a decreasing effect on the predictions, whereas, at greater thickness values, the addition of the jacket thickness increases the model predictions. The effect of the specimen length on the predicted \(\mathrm{\epsilon_{cu}}\) values is similar to the case of the \(\mathrm{f_{cc}}\) prediction, such that, at higher levels of specimen length, the addition of this variable into the models decreases the predicted \(\mathrm{\epsilon_{cu}}\) values. Figure 13b shows that, up to 80 MPa, an increase in \(\mathrm{f_{co}}\) leads to lower SHAP values of  
Figure 10: SHAP summary plot of the CatBoost model for \(\mathrm{f_{cc}}\) prediction. this variable, which indicates a decreasing effect on the ultimate strain predictions. For f\({}_{\rm co}\) values greater than 80 MPa, the SHAP values of f\({}_{\rm co}\) are spread in a wider range.