# Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR
## 1 Introduction
[PERSON]'s [14] quantitative analysis confirmed that increasing AEA content from 0% to 0.7% increases large pores by 3.56% and capillary pores by 3.05%; at 1.4%, large and capillary pores exceed 50% of total porosity, significantly affecting concrete performance. Despite the research on individual parameter effects, knowledge gaps persist in understanding their combined influence on simultaneously optimizing the flexural-compressive strength ratio, controlling shrinkage, and regulating porosity under extreme conditions. Traditional optimization methods addressing single parameters or employing subjective weighting techniques struggle to effectively resolve multi-objective conflicts. More advanced intelligent design methods are needed to address these complex engineering challenges. Intelligent design methods have emerged as effective tools for solving complex engineering problems. Response Surface Methodology (RSM), which integrates experimental design with mathematical modeling, has been widely applied to optimize concrete performance models [16; 17; 18]. RSM is effective in modeling non-linear relationships between mix proportion parameters and performance indicators [19]. However, when applied to concrete design for extreme arid environments, practical optimization problems involve multiple conflicting objectives that cannot be resolved through traditional approaches, such as balancing cost and performance or durability and strength. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) generates high-quality Pareto-optimal solutions by improving the uniformity of objective distributions, providing diverse trade-offs for multi-objective optimization [20; 21; 22]. Despite its advantages, NSGA-III often produces a large and widespread solution set, making it challenging to identify the optimal solution with the best overall performance. To address this challenge, multi-criteria decision-making methods have been employed for selecting solutions from Pareto fronts. Challenges remain in scientifically allocating weights and interpreting results. Traditional weighting methods, such as expert scoring and entropy weighting, rely on subjective experience or single characteristics, failing to comprehensively reflect the relative importance of different indicators. Criteria Importance Through Intercriteria Correlation (CRITIC) combines the standard deviation and correlation of indicators to objectively evaluate their importance, avoiding subjective bias. It has achieved significant success in other fields of multi-objective optimization, such as material optimization and environmental system management [23; 24]. VISekriterijumsko KOmpro-misono Rangiranje (VIKOR), a compromise ranking approach, has been successfully applied in areas such as mechanical design and energy planning. VIKOR ranks multi-objective solution sets reliably, even in scenarios with conflicting criteria or unclear preferences, demonstrating strong decision-making potential [25; 26]. However, many existing studies have applied techniques in isolation, lacking an integrated framework for intelligent design, particularly in multi-objective optimization for pavement concrete mix proportions. To bridge the research gaps, this study develops an integrated optimization framework that systematically addresses the multi-objective challenges of pavement concrete design.