## 2 Data Source Integrity and Parametric Space Definition
### Data Source Integrity and Inclusion Criteria  
The selection of value ranges for five key input variables--Transport Distance (X1), Processing Energy Intensity (X2), Moisture Content (X3), Gradation Adjustment Level (X4), and Electricity Grid CO2 Emission Factor (X5)--was guided by published case studies, experimental datasets, and technical standards. The five selected input variables (X1-X5) were chosen for their strong influence on cost and emissions, data availability, and ability to indirectly capture more complex phenomena. For instance, energy intensity accounts for the drying process, which varies with climate and fuel source, while transport distance captures first-order effects of road quality and vehicle type. This parsimonious input design supports tractable surrogate modeling while ensuring broad applicability. Future studies may explicitly model additional context-specific variables where fine-grained data become available. To support this selection strategy, the value ranges used in this study were grounded in empirical and policy-based sources from the literature. For example, the transport distance range of 5-250 km reflects values reported in regional aggregate logistics studies [23; 24]; energy consumption for crushing and drying operations (0.65-2.1 kWh/ton) was derived from empirical facility data [25]; grid emission factors (0.24-1.05 kg CO2/kWh) correspond to national electricity mix profiles published by [26]; and moisture content values (2-15%) reflect typical open-air storage in various climatic zones [27]. To ensure methodological consistency, only sources that provided quantitative, disaggregated data (e.g., by process stage or energy type) were considered in defining the parameter ranges. Mixtures containing blended additives (e.g., fly ash, cement) or modified chemistries were excluded in order to isolate the independent contribution of steel slag. This process enabled the construction of a dimensionally consistent input space, with all variables normalized to a per-volume (m3) basis for comparability. Following the definition of this parametric domain, a total of 530 candidate configurations were generated using a stratified uniform sampling approach, in which each input variable was sampled independently across its literature-supported range using equally spaced intervals. The discretization scheme consisted of the following:  
* X\({}_{1}\): Transport Distance (km)--10 levels ranging from 5 to 250 km, with a step size of 27.2 km. * X\({}_{2}\): Energy Intensity (kWh/ton)--6 levels between 0.8 and 2.0 kWh/ton, increasing by 0.24 kWh/ton per level. * X\({}_{3}\): Moisture Content (%)--6 levels from 2% to 12%, with a step size of 2%. * X\({}_{4}\): Gradation Adjustment (categorical)--3 levels (0: none, 1: moderate, 2: major)
* X\({}_{5}\): Grid CO\({}_{2}\) Factor (kg/kWh) -9 levels between 0.3 and 1.0 kg CO\({}_{2}\)/kWh, incremented by 0.0875 kg CO\({}_{2}\)/kWh. Rather than employing full-factorial enumeration (which would yield over 9000 combinations), a randomized stratified sampling method was used to generate a manageable yet well-distributed set of 482 distinct input configurations. This technique enabled adequate space-filling while ensuring statistical representation of each variable's range without overwhelming computational demand. In addition, all generated combinations were reviewed for engineering feasibility. Implausible scenarios--such as configurations with both very low energy use and very high moisture content, which are physically inconsistent due to drying energy demands--were filtered out prior to model training. After the exclusion of such cases, the resulting dataset comprised 482 technically feasible and literature-consistent scenario profiles. The finalized descriptive statistics are reported in Table 1, which lists the minimum, maximum, mean, and standard deviation for each engineered input parameter. Each variable has also been annotated with the associated data source domain, ensuring full traceability and scientific transparency.