## 4 Conclusions  
In this research, classification-based regression methods based on the RF, M5 rule, M5p, and CHAID techniques were applied as a ML tools to develop new predictive models of the mechanical characteristics of RCCP. The models were constructed using comprehensive datasets of RCCP design codes. Before development of the models, PCA was applied to determine the most important inputs predictors for data dimension reduction. RF and CHAID presented better performance for the training dataset compared to the other methods utilized in this research. The higher rank of RF and CHAID \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Model**} & \multicolumn{2}{c}{-} & \multicolumn{1}{c}{\(\mathbf{S_{e}}\)} & \multicolumn{1}{c}{**Median**} & \multicolumn{1}{c}{**MAD**} & \multicolumn{1}{c}{**Uncertainty (\%)**} \\ \hline \multirow{4}{*}{**CS**} & RF & 0.174 & 8.765 & 34.181 & 11.302 & 33.065 \\  & M5 rule & \(-\)0.017 & 3.313 & 32.539 & 12.530 & 38.509 \\  & M5p & 0.221 & 6.533 & 33.789 & 12.374 & 36.622 \\  & CHAID & 0.499 & 7.527 & 33.724 & 11.210 & 33.240 \\ \hline \multirow{4}{*}{**TS**} & RF & 0.004 & 0.004 & 3.168 & 0.836 & 26.393 \\  & M5 rule & \(-\)0.038 & 0.361 & 3.091 & 0.973 & 31.507 \\  & M5p & \(-\)0.012 & 0.201 & 3.108 & 0.943 & 30.346 \\  & CHAID & 0.048 & 0.578 & 3.116 & 0.883 & 28.348 \\ \hline \multirow{4}{*}{**FS**} & RF & \(-\)0.026 & 0.905 & 4.586 & 1.377 & 30.026 \\  & M5 rule & 0.104 & 0.754 & 4.568 & 1.428 & 31.275 \\ \cline{1-1}  & M5p & 0.008 & 0.338 & 4.563 & 1.455 & 31.896 \\ \cline{1-1}  & CHAID & 0.188 & 0.798 & 4.806 & 1.447 & 30.119 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Uncertainty quantification for all classification models. Figure 10: Sensitivity analysis of variable importance. for the training data indicates that their flexibility, a result of combining multiple decision trees, is particularly useful for estimating the mechanical properties of RCCP. The performance of RF was significantly better than the other classification-based regression methods. This difference may be due to the larger diversity among the learned trees of RF, which is a consequence of RF's implementation for randomized splitting at nodes. Typically, classification-based regression methods function better if there is notable diversity among the models [85, 86]. However, the performance of the M5 rule and M5p models was inferior to both RF and CHAID. This may be because the M5 rule and M5p methods are more prone to overfitting, while RF and CHAID focus on variance reduction and consequently avoid overfitting. According to results of this research, the following conclusions can be drawn:  
* Developing models based on RF, M5 rule, M5p, and CHAID revealed that the CS, TS, and FS of RCCP are mainly related to the six inputs of CA, FA, SCM, W, B, and AS, as determined by PCA. * The presented CS, TS, and FS-simulated values indicate that the RF method has greater precision compared with the other three tree-based techniques, with respect to R, NSE, RMSE, and RSD measures for the training and testing phases. * The proposed RF and CHAID models met all of the required criteria of external validation. * The Monte-Carlo uncertainty investigation of the implemented tree-based methods validated their robustness. Moreover, sensitivity analysis of variable importance revealed fine aggregate content to be the most important predictor influencing the mechanical characteristics of RCCP. Conceptualization, A.A. and M.J.T.A. ; Data curation, A.A., M.Y.-c. and N.N. ; Formal analysis, A.A., M.A.-s. and A.M.; Funding acquisition, A.A., M.Y.-c. and N.N. ; Investigation, A.A. and M.J.T.A. ; Methodology, M.A.-s. and A.M.; Project administration, A.A. and M.Y.-c., P.M., and N.N. ; Resources, M.J.T.A. ; Software, A.A., P.M. and M.J.T.A. ; Supervision, A.M.; Validation, N.N. ; Visualization, A.A., and A.M.; Writing--original draft, M.A.-s. and A.A.; Writing--review & editing, A.M. All authors have read and agreed to the published version of the manuscript.