Enhancing Robustness in Precast Modular Frame Optimization: Integrating NSGA-II, NSGA-III, and RVEA for Sustainable Infrastructure

Andrés Ruiz-Vélez, José García, Julián Alcalá and Víctor Yepes ()
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Andrés Ruiz-Vélez: Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 València, Spain
José García: School of Construction and Transportation Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile
Julián Alcalá: Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 València, Spain
Víctor Yepes: Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 València, Spain

Mathematics, 2024, vol. 12, issue 10, 1-30

Abstract: The advancement toward sustainable infrastructure presents complex multi-objective optimization (MOO) challenges. This paper expands the current understanding of design frameworks that balance cost, environmental impacts, social factors, and structural integrity. Integrating MOO with multi-criteria decision-making (MCDM), the study targets enhancements in life cycle sustainability for complex engineering projects using precast modular road frames. Three advanced evolutionary algorithms—NSGA-II, NSGA-III, and RVEA—are optimized and deployed to address sustainability objectives under performance constraints. The efficacy of these algorithms is gauged through a comparative analysis, and a robust MCDM approach is applied to nine non-dominated solutions, employing SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR decision-making techniques. An entropy theory-based method ensures systematic, unbiased criteria weighting, augmenting the framework’s capacity to pinpoint designs balancing life cycle sustainability. The results reveal that NSGA-III is the algorithm converging towards the most cost-effective solutions, surpassing NSGA-II and RVEA by 21.11% and 10.07%, respectively, while maintaining balanced environmental and social impacts. The RVEA achieves up to 15.94% greater environmental efficiency than its counterparts. The analysis of non-dominated solutions identifies the A 4 design, utilizing 35 MPa concrete and B500S steel, as the most sustainable alternative across 80% of decision-making algorithms. The ranking correlation coefficients above 0.94 demonstrate consistency among decision-making techniques, underscoring the robustness of the integrated MOO and MCDM framework. The results in this paper expand the understanding of the applicability of novel techniques for enhancing engineering practices and advocate for a comprehensive strategy that employs advanced MOO algorithms and MCDM to enhance sustainable infrastructure development.

Keywords: multi-objective optimization; multi-criteria decision-making; NSGA-II; NSGA-III; RVEA; SAW; FUCA; TOPSIS; PROMETHEE; VIKOR (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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