## 3 Results and Discussion
### Comparison with Mechanics-Driven Models  
The performance of the CB model was further evaluated against three traditional mechanics-driven models, which are outlined in Appendix A. Accordingly, the SS of RCDBs was predicted using the four models for the entire database. The scatter plot in Figure 11a-d illustrates the distribution of the predicted SS to experimental SS ratios (\(V_{u}\), _Pred./Vu_, _Exp._) with respect to \(a/d\), where the different types of RCDBs (i.e., WOR, WHR, WVR, WHVR) are distinguished by different colors. More information on the predictive performances of the four models are revealed in Table 6. It is observed that the performance of traditional models significantly varies. According to the mean values of (\(V_{u}\), _Pred./Vu_, _Exp._), CSA A23.3 and EU2 tended to underpredict the strength, while ACI 318 tended to overpredict the SS of RCDBs. In addition, the \(COV\) values of these three mechanics-driven models are considerably high compared to the CB model. As a comparison, the CB model even produced highly accurate predictions with a mean value of 1.014, an \(SD\) value of 0.116, and a \(COV\) value of only 11.457%. Table 7 also lists the predictive performances of the four models considering different types of RCDBs. As seen from Table 7, it is clear that the CB model performed reliably and accurately regardless of web reinforcement detail. It obtained mean values of (\(V_{u}\), _Pred./Vu_, _Exp._) between 1.004 and 1.024, \(SD\) values between 0.071 and 0.145, and \(COV\) values between 7.090% and 14.135%. Figure 10: REC curves for optimized ML models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Datapoints** & **Input Features** & **Best Model** & \(\mathbf{R^{2}}\) \\ \hline [PERSON] et al. [9] & 271 & 16 & XGBoost & 0.928 \\ [PERSON] et al. [10] & 320 & 14 & XGBoost & 0.936 \\ [PERSON] et al. [11] & 457 & 9 & XGBoost & 0.917 \\ [PERSON] et al. [12] & 518 & 15 & GPR & 0.937 \\ [PERSON] et al. [12] & 271 & 16 & XGBoost & 0.928 \\ [PERSON] et al. [99] & 386 & 22 & RF & 0.937 \\ This study & 950 & 16 & CatBoost & 0.942 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with previous ML models in predicting the SS of RCDBs. In short, this research proved that ML approaches have better performance and reliability than mechanics-driven approaches of standard codes. This is due to the fact that most traditional approaches are derived based on some assumptions or simplifications that fit particular applications. Furthermore, traditional approaches typically consider a safety factor in the formulation to mitigate the unknown uncertainties in design. Meanwhile, ML approaches do not require any assumptions or simplifications. The relationships between input and output features are detected by their inherent nature; hence, more complex behaviors are captured. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & \multicolumn{6}{c}{\(\mathbf{V_{u}}\)_Pred./\(\mathbf{V_{u}}\)_Exp._} \\ \hline  & **Min** & **Max** & **Mean** & **SD** & **_COV_ (\%)_ \\ \hline \hline \multirow{2}{*}{ACI 318} & 0.252 & 7.606 & 1.042 & 0.570 & 54.717 \\  & 0.133 & 2.873 & 0.763 & 0.353 & 46.176 \\  & 0.234 & 3.784 & 0.886 & 0.419 & 47.339 \\  & 0.573 & 2.388 & 1.014 & 0.116 & 11.457 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Predictive performances of mechanics-driven and CB models.