## 3 Results
### Performances of the ML Models  
The confined compressive strength values predicted by the ML models are plotted against the experimental measurements in Figure 6. The predictions on the training set and the test set are plotted in different colors in each plot. Also, the \(\pm\)10% deviation lines from a perfect match between the actual and predicted values are shown on each plot. The performances of these ML models have been quantified using the same four error metrics used to determine the accuracy of the equations in Tables 4 and 5. Among these four metrics, a R\({}^{2}\) score value close to one indicates high prediction accuracy. In addition to their accuracy of prediction, the ML models have also been investigated with respect to their computational speed. According to the listed values in Table 6, the extra trees model performed best on the test set, in terms of all error metrics. In terms of computational speed, the support vector regressor (SVR) model was the fastest, although this model performed significantly worse than the remaining models in terms of prediction accuracy. Among the five ensemble learning models listed in Table 6, LightGBM was the worst-performing model in terms of all error metrics. Figure 7 shows the first of the 100 decision trees constituting the XGBoost model developed for the prediction of f\({}_{\text{cc}}\). In Figure 7, the root node tests the value of the unconfined ultimate strength (f\({}_{\text{co}}\)). It should be mentioned that the values in Figure 7 are scaled values, except for the values in the leaf nodes. If the condition inside the root node or an internal node is true, or the feature value is missing in a particular data sample, then the node branches towards the left-hand side; otherwise, it branches towards the right-hand side. The tree consists of 27 internal nodes, splitting at different levels of AFRP jacket thickness (t\({}_{\text{f}}\)), unconfined ultimate strength (f\({}_{\text{co}}\)), elasticity modulus of the AFRP wrap (E\({}_{\text{f}}\)), specimen diameter (D), specimen length (L) and AFRP ultimate tensile strength (f\({}_{\text{f}}\)). There are 29 green colored leaf nodes, one of which is selected as the tree output, depending on the outputs of the previous internal nodes and the root node. By adding up all of the decision tree outputs, the XGBoost model's output is determined. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**MAE [MPa]**} & \multicolumn{2}{c}{**RMSE [MPa]**} & \multicolumn{2}{c}{**Duration**} \\ \cline{2-7}  & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **[s]** \\ \hline XGBoost & 0.9792 & 0.9371 & 2.530 & 6.135 & 7.080 & 12.16 & 5.24 \\ CatBoost & 0.9232 & 0.9064 & 9.965 & 10.01 & 13.61 & 14.84 & 12.62 \\ Random Forest & 0.8729 & 0.9228 & 13.17 & 8.343 & 17.52 & 13.47 & 6.47 \\ LightGBM & 0.7894 & 0.8837 & 17.10 & 11.65 & 22.55 & 16.54 & 9.59 \\ Extra trees & 0.7999 & 0.9383 & 1.812 & 5.438 & 6.958 & 12.05 & 7.26 \\ KNN & 0.9510 & 0.9090 & 3.091 & 6.182 & 10.35 & 14.63 & 4.70 \\ SVR & 0.2849 & 0.3063 & 30.74 & 34.70 & 40.29 & 43.16 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model performances on compressive strength prediction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equation** & **R\({}^{2}\)** & **MAE [MPa]** & **RMSE [MPa]** \\ \hline [PERSON] et al. [21] & 0.7610 & 31.34 & 43.17 \\ [PERSON] and [PERSON] [22] & 0.6592 & 72.44 & 104.8 \\ [PERSON] and [PERSON] [23] & 0.6527 & 41.79 & 51.48 \\ [PERSON] et al. [24] & 0.7647 & 29.56 & 41.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Equation performances for compressive strength prediction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equation** & **R\({}^{2}\)** & **MAE [\%]** & **RMSE [\%]** \\ \hline [PERSON] et al. [21] & 0.1869 & 0.9622 & 1.186 \\ [PERSON] et al. [24] & 0.0030 & 1.638 & 1.909 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Equation performances for ultimate strain prediction.