## 5 Conclusions  
This study developed a robust, machine learning-based multi-objective optimization (MOO) framework that integrates tree-based ensemble modeling with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to quantitatively assess and balance economic and environmental performance in the use of steel slag as road base material. Through a systematic combination of predictive analytics, feature interpretability, and Pareto-based optimization, the framework offers an actionable decision support structure for infrastructure planners navigating cost-emission trade-offs. The primary technical conclusions of this study are as follows:  
Modeling accuracy and predictive reliability: Among the evaluated ensemble methods, the Gradient Boosted Regressor (GBR) achieved the highest predictive performance for both cost and CO2 emissions, with R2 values of 0.962 (cost) and 0.955 (CO2), and RMSE values below 7.5 units on a 20% holdout test set. These results confirm the capacity of GBR to capture complex, nonlinear interactions in high-dimensional infrastructure logistics datasets. The use of Bayesian optimization for hyperparameter tuning further improved convergence and model generalizability. Feature-level sensitivity and interaction patterns: SHAP analysis revealed that Transport Distance (x1) and Energy Intensity (x2) are the most influential features in cost determination, whereas Moisture Content (x3) and grid CO2 Emission Factor (x5) dominate the emission prediction landscape. Notably, the cross-dependence between x2 and x5--where high processing demand under carbon-intensive grids led to extreme emissions--emphasizes the need for joint optimization of operational and locational parameters in material supply chains. Multi-objective trade-off characterization: The NSGA-II algorithm produced a well-distributed Pareto front comprising 174 non-dominated configurations, covering a cost range of 18.5-41.2 USD/m3 and CO2 emissions from 45.6 to 138.2 kg/m3. This empir  
Figure 13: Pareto frontier with uncertainty envelope from 500 stochastic realizations. ical front confirms the existence of nonlinear, non-convex trade-off regions, including clearly observable knee points where marginal increases in cost lead to significant emission savings--aligning with similar findings in MOO-based materials research  
Optimal solution identification via TOPSIS: Using distance-based ranking, a configuration with 24.6 USD/m\({}^{3}\) cost and 74.8 kg/m\({}^{3}\) CO\({}_{2}\) emission was identified as the most balanced solution (closeness coefficient = 0.931). This configuration features moderate transport distance (47 km), mid-level energy intensity (1.21 kWh/ton), and typical grid carbon intensity (0.47 kg/kWh), making it both technologically realistic and operationally replicable. Sensitivity analysis confirmed the stability of the proposed configuration under \(\pm\)10% input variability, validating its robustness for practical deployment. Recommended Pareto Solution and Practical Implications: From a practical perspective, one of the mid-range configurations on the Pareto front was identified as the most applicable solution, offering a reasonable compromise between cost and CO\({}_{2}\) emissions. This solution represents neither the lowest-cost nor the lowest-emission alternative, but rather it balances both objectives in a technologically realistic and logistically feasible manner. It reflects moderate values across key decision variables such as transport distance, energy use, and grid carbon intensity--conditions commonly encountered in real-world infrastructure projects. Its selection supports practical implementation in regions where both economic constraints and environmental regulations must be simultaneously considered. Scalability and transferability of the framework: Although this study focused on BOF slag in road base applications, the proposed MOO-SHAP framework is generalizable to other recycled or industrial by-product materials, such as copper slag, bottom ash, or fly ash composites.