## 4 Results and Discussion
### Predictive Model Performance and Interpretation  
The predictive accuracy of the four selected regression models--Random Forest Regressor (RFR), Extremely Randomized Trees (ERT), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)--was evaluated on the held-out test set for both performance targets: total cost and CO\({}_{2}\) emissions. As shown in Table 2, GBR achieved the highest Pearson correlation coefficients (R = 0.962 for cost and 0.955 for emission), along with the lowest RMSE and MAE values, confirming its superior ability to capture nonlinear patterns in the engineered dataset. Furthermore, the a20 index--which indicates the percentage of predictions within \(\pm\)20% of the actual values--was highest for GBR, reaching 87.5% for cost and 85.2% for emissions, suggesting excellent practical reliability in real-world applications. These visual observations corroborate the statistical findings reported in Table 2, particularly the high Pearson R values (>0.95) and low RMSE, confirming that the GBR model maintains both precision and consistency across a wide distribution of material and logistical configurations. Recent studies have explored machine learning techniques for predicting various properties of concrete and pavement materials. Gradient Boosting Regression (GBR) has shown superior performance in predicting pavement damage [59] and asphalt pavement performance indices [60]. Random Forest Regression (RFR) and eXtreme Gradient Boosting (XGBR) have demonstrated excellent capabilities in predicting Marshall mix properties and resilient modulus of stabilized base materials [33]. To confirm the statistical significance of performance differences, a Wilcoxon signed-rank test was applied to the cross-validated RMSE values across five folds. Results indicate that GBR significantly outperformed all other models in both prediction tasks (\(p<0.01\)), confirming that its superior performance is not due to random variation. The predictive capability of the GBR model was further examined through a visual comparison of predicted versus actual values for both performance indicators, as illustrated in Figure 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Output** & **R** & **MAE** & **RMSE** & **a20 (\%)** \\ \hline \multirow{2}{*}{RFR} & Cost (USD/m\({}^{3}\)) & 0.951 & 4.62 & 6.34 & 82.1 \\  & CO\({}_{2}\) (kg/m\({}^{3}\)) & 0.938 & 7.25 & 9.17 & 79.3 \\ \hline \multirow{2}{*}{ERT} & Cost (USD/m\({}^{3}\)) & 0.949 & 4.49 & 6.11 & 81.7 \\  & CO\({}_{2}\) (kg/m\({}^{3}\)) & 0.942 & 6.98 & 8.86 & 80.4 \\ \hline \multirow{2}{*}{GBR} & Cost (USD/m\({}^{3}\)) & 0.962 & 3.84 & 5.23 & 87.5 \\  & CO\({}_{2}\) (kg/m\({}^{3}\)) & 0.955 & 5.72 & 7.44 & 85.2 \\ \hline \multirow{2}{*}{XGBR} & Cost (USD/m\({}^{3}\)) & 0.960 & 4.07 & 5.34 & 86.2 \\  & CO\({}_{2}\) (kg/m\({}^{3}\)) & 0.954 & 6.01 & 7.62 & 84.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Predictive performance of tree-based ensemble models on the test set. The cost prediction plot (top panel) exhibits a concentrated scatter distribution along the 45-degree identity line, indicating a strong agreement between predicted and observed values across the test set. Deviations from the diagonal are minimal, and no systematic under- or over-prediction is evident throughout the full range of observed costs (15-50 USD/m\({}^{3}\)). Similarly, in the emission prediction plot (bottom panel), the points closely follow the ideal fit, with the majority of residuals confined within \(\pm\)10 kg/m\({}^{3}\) of the actual values, even in higher emission regions approaching 140 kg/m\({}^{3}\). Figure 3: The cost and emission prediction plots. These visual and statistical results are in line with previous studies demonstrating the superior generalization capacity of gradient boosting models in civil infrastructure domains. For instance, [PERSON] et al.