## 3 Results
### Performances of the ML Models
Figure 6: ML predictions of the compressive strength using (**a**) XGBoost, (**b**) CatBoost, (**c**) Random Forest, (**d**) LightGBM, (**e**) Extra Trees, and (**f**) KNN. Figure 8 and Table 7 present the performances of the ML models in predicting the ultimate strain of AFRP-confined concrete specimens. Due to the data availability limitations, a reduced dataset, consisting of 159 samples, was used in the generation of the synthetic dataset. This dataset reduction was caused by missing values for the unconfined concrete ultimate strain. For the training and testing of the ML models, a dataset consisting of 477 samples was used, which consisted of both synthetic and real data points. Clearly, the data quality of the model is not affected by the presence of a large number of samples. Figure 7: First tree of the XGBoost model for the prediction of \(\mathrm{f_{cc}}\). the predictions for most of the specimens in the database with 477 samples lie within 10% distance from the target values. Also, Table 7 shows that the R\({}^{2}\) score for all of the ML models was greater than 0.7 on the test set. Furthermore, excluding the SVR algorithm, minimum R\({}^{2}\) scores of 0.8837 and 0.8155 can be observed for compressive strength and ultimate strain, respectively. In terms of the mean absolute error metric, the most accurate predictions on the test set were made by the extra trees regressor, followed by the K-nearest-neighbors (KNN) algorithm. In terms of R\({}^{2}\) score, the XGBoost model was the second-best-performing model in the prediction of the ultimate strain. The performances of the different ML models and the equations are also presented, with Taylor diagrams for both ultimate strength and ultimate strain cases, in Figure 9, where each set of predictions is represented with a dot of different color. In Figure 9, the standard deviations of each set of predictions are shown on the horizontal and vertical axes. The standard deviation value, corresponding to a particular prediction set, can be found with the help of circular arcs. In each Taylor diagram, the original dataset is represented with a blank dot. The Pearson correlation values for each prediction set are shown on the radial axis. Figure 8: _Cont._  
Figure 8: ML predictions for the ultimate strain using (**a**) XGBoost, (**b**) CatBoost, (**c**) Random Forest, (**d**) LightGBM, (**e**) Extra Trees, and (**f**) KNN. \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 [\%]**} & \multicolumn{2}{c}{**RMSE [\%]**} & \multicolumn{1}{c}{**Duration**} \\ \cline{2-9}  & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **[s]** \\ \hline XGBoost & 0.9966 & 0.9879 & 0.0303 & 0.0708 & 0.0579 & 0.1076 & 5.64 \\ CatBoost & 0.9508 & 0.9385 & 0.1677 & 0.1857 & 0.2169 & 0.2471 & 15.37 \\ Random Forest & 0.9694 & 0.8863 & 0.1306 & 0.2606 & 0.1711 & 0.3362 & 7.93 \\ LightGBM & 0.9242 & 0.8155 & 0.2228 & 0.3378 & 0.2693 & 0.4278 & 11.59 \\ Extra trees & 0.9962 & 0.9899 & 0.0117 & 0.0485 & 0.0480 & 0.0979 & 5.16 \\ KNN & 0.9968 & 0.9862 & 0.0129 & 0.0537 & 0.0564 & 0.1148 & 4.28 \\ SVR & 0.8547 & 0.7084 & 0.2628 & 0.4445 & 0.3640 & 0.5665 & 3.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model performances in the ultimate strain prediction. Figure 9: Taylor diagrams for the prediction of (**a**) ultimate strength and (**b**) ultimate strain.