Enhanced Structural Design of Prestressed Arched Trusses through Multi-Objective Optimization and Multi-Criteria Decision-Making

Andrés Ruiz-Vélez, José García, Gaioz Partskhaladze, 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: Escuela de Ingeniería de Construcciόn y Transporte, Pontificia Universidad Catόlica de Valparaíso, Valparaíso 2362804, Chile
Gaioz Partskhaladze: Engineering and Construction Department, Faculty of Technologies, Batumi Shota Rustaveli State University, 35/32, Ninoshvili/Rustaveli Str., 6010 Batumi, Georgia
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 16, 1-30

Abstract: The structural design of prestressed arched trusses presents a complex challenge due to the need to balance multiple conflicting objectives such as structural performance, weight, and constructability. This complexity is further compounded by the interdependent nature of the structural elements, which necessitates a comprehensive optimization approach. Addressing this challenge is crucial for advancing construction practices and improving the efficiency and safety of structural designs. The integration of advanced optimization algorithms and decision-making techniques offers a promising avenue for enhancing the design process of prestressed arched trusses. This study proposes the use of three advanced multi-objective optimization algorithms: NSGA-III, CTAEA, and SMS-EMOA, to optimize the structural design of prestressed arched trusses. The performance of these algorithms was evaluated using generational distance and inverted generational distance metrics. Additionally, the non-dominated optimal designs generated by these algorithms were assessed and ranked using multiple multi-criteria decision-making techniques, including SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR. This approach allowed for a robust comparison of the algorithms and provided insights into their effectiveness in balancing the different design objectives. The results of the study indicated that NSGA-III exhibited superior performance with a GD value of 0.215, reflecting a closer proximity of its solutions to the Pareto front, and an IGD value of 0.329, indicating a well-distributed set of solutions across the Pareto front. In comparison, CTAEA and SMS-EMOA showed higher GD values of 0.326 and 0.436, respectively, suggesting less convergence to the Pareto front. However, SMS-EMOA demonstrated a balanced performance in terms of constructability and structural weight, with an IGD value of 0.434. The statistical significance of these differences was confirmed by the Kruskal–Wallis test, with p -values of 2.50 × 10 − 15 for GD and 5.15 × 10 − 06 for IGD. These findings underscore the advantages and limitations of each algorithm, providing valuable insights for future applications in structural optimization.

Keywords: multi-objective optimization; multi-criteria decision-making; NSGA-III; CTAEA; SMS-EMOA; 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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