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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Andrés Ruiz-Vélez & José García & Gaioz Partskhaladze & Julián Alcalá & Víctor Yepes, 2024. "Enhanced Structural Design of Prestressed Arched Trusses through Multi-Objective Optimization and Multi-Criteria Decision-Making," Mathematics, MDPI, vol. 12(16), pages 1-30, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2567-:d:1459912
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    References listed on IDEAS

    as
    1. Beume, Nicola & Naujoks, Boris & Emmerich, Michael, 2007. "SMS-EMOA: Multiobjective selection based on dominated hypervolume," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1653-1669, September.
    2. Behzadian, Majid & Kazemzadeh, R.B. & Albadvi, A. & Aghdasi, M., 2010. "PROMETHEE: A comprehensive literature review on methodologies and applications," European Journal of Operational Research, Elsevier, vol. 200(1), pages 198-215, January.
    3. Rivero-Iglesias, Jose M. & Puente, Javier & Fernandez, Isabel & León, Omar, 2023. "Integrated model for the assessment of power generation alternatives through analytic hierarchy process and a fuzzy inference system. Case study of Spain," Renewable Energy, Elsevier, vol. 211(C), pages 563-581.
    4. Nihan Kabadayi & Mohammad Dehghanimohammadabadi, 2022. "Multi-objective supplier selection process: a simulation–optimization framework integrated with MCDM," Annals of Operations Research, Springer, vol. 319(2), pages 1607-1629, December.
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