## 1 Introduction  
Natural aggregates have long been the principal component in road base layers due to their mechanical reliability, availability, and cost-effectiveness. However, the ever-increasing global demand for infrastructure, combined with the rapid depletion of natural resources, has led to growing environmental concerns and logistical challenges in aggregate sourcing and transportation [1; 2]. Among various alternatives, steel slag, a by-product of steelmaking processes, has emerged as a promising substitute for natural aggregates owing to its favorable geotechnical properties, high abrasion resistance, and potential for circular material flow [3; 4; 5]. Nevertheless, the large-scale utilization of steel slag in civil infrastructure is hindered by multiple interrelated factors, such as the transportation distance between slag production sites and construction locations, energy-intensive processing requirements, and associated carbon emissions [6; 7]. In sustainable pavement engineering, minimizing both environmental impact and economic cost is a crucial challenge, particularly when materials like steel slag are sourced far from end-use locations. In this context, the optimization of material logistics and processing strategies plays a central role in enhancing both environmental performance and cost efficiency [8]. Transportation emissions, which can represent over 50% of the life-cycle environmental impact for heavy materials, are strongly influenced by the haul distance and mode of transport [9]. Additionally, the degree of processing required--such as crushing, sieving, and moisture conditioning--significantly contributes to both operational cost and embodied carbon, particularly when regional electricity mixes are carbon-intensive [10]. Therefore, a systematic method is needed to explore trade-offs between conflicting objectives such as transportation cost, energy consumption, and emissions. Traditional decision-making approaches for aggregate selection and logistics planning have largely relied on rule-of-thumb heuristics, linear cost minimization, or laboratory-based scenario testing [11]. These methods often fail to capture the complex, nonlinear relationships between logistics parameters and life-cycle costs or emissions, especially when applied to large datasets across varying geographies and supply chain conditions. In line with emerging trends in sustainable construction materials, multi-objective optimization techniques such as response surface modeling have gained traction for balancing conflicting objectives, as demonstrated in recent material design studies [12]. Moreover, deterministic models are generally incapable of generating Pareto-optimal solutions when multiple objectives are in conflict [13]. To overcome these limitations, machine learning (ML) techniques have recently been introduced to model complex relationships in civil and materials engineering [14; 15; 16]. In particular, tree-based ensemble learning methods such as Random Forest Regressor (RFR), Extremely Randomized Trees (ERT), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting (XGBR) have shown strong predictive performance in handling heterogeneous, high-dimensional datasets due to their robustness to overfitting and ability to capture nonlinearity [17; 18]. Once a reliable predictive model is established, advanced multi-objective optimization algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) can be employed to identify sets of optimal solutions that balance competing objectives without reducing the problem to a single aggregated metric [19; 20]. This study proposes a novel machine learning-based multi-objective optimization (MOO) framework for evaluating steel slag utilization in road base layers. A comprehensive dataset comprising 482 scenarios was compiled from existing literature and engineering records, incorporating variables such as transport distance, processing energy intensity, slag gradation, moisture content, and local energy source mix.