EAAI Journal 2026 Journal Article
Engineering application of non-dominated sorting genetic algorithm III: Multi-objective optimization of ultra-high performance concrete for diverse scenarios
- Wei Zhang
- Zhenhua Duan
- Yuqing Wu
- Chao Liu
- Yizhou Yao
- Ahmed Nasr
- Qingmei Yang
- Huiyu Xia
This study addresses the technical limitations of conventional mix design methods for ultra-high performance concrete (UHPC) concerning multi-objective synergistic optimization and diverse scenarios adaptability. Leveraging 2824 experimental data points, a comprehensive prediction system was established for mechanical properties, workability and durability. The prediction performance of ten machine learning algorithms was systematically evaluated, and the SHapley Additive exPlanations (SHAP) method was used to elucidate the influence mechanism of crucial features. Furthermore, a comprehensive collaborative optimization framework for UHPC under typical engineering scenarios was developed by integrating the non-dominated sorting genetic algorithm III (NSGA-III) with the technique for order preference by similarity to ideal solution (TOPSIS) decision-making model, and visualization technology was integrated to construct a graphical user interface (GUI) system. The results demonstrate that the NSGA-III algorithm achieved continuous hypervolume (HV) improvement within 500 generations, while the spacing indicator decreased rapidly in the initial iterations, confirming its capability to approximate the actual Pareto front through adaptive crossover-mutation strategies and elitism preservation. The developed ‘data driven - performance prediction - multi objective optimization - decision analysis' technical system, provides a quantifiable and scalable solution for addressing multi-objective optimization challenges in engineering materials.