Steel and Composite Structures

  • 제44권5호
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  • Pages.603-617
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  • 2022
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
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Discrete sizing and layout optimization of steel truss-framed structures with Simulated Annealing Algorithm

  • Bresolin, Jessica M. (Faculty of Engineering and Architecture, Graduate Program in Civil and Environmental Engineering (PPGEng), University of Passo Fundo (UPF)) ;
  • Pravia, Zacarias M.C. (Faculty of Engineering and Architecture, Graduate Program in Civil and Environmental Engineering (PPGEng), University of Passo Fundo (UPF)) ;
  • Kripka, Moacir (Faculty of Engineering and Architecture, Graduate Program in Civil and Environmental Engineering (PPGEng), University of Passo Fundo (UPF))
  • 투고 : 2021.08.06
  • 심사 : 2022.08.30
  • 발행 : 2022.09.10
⟨ 이전 논문 다음 논문 ⟩

초록

Structural design, in general, is developed through trial and error technique which is guided by standards criteria and based on the intuition and experience of the engineer, a context that leads to structural over-dimensioning, with uneconomic solutions. Aiming to find the optimal design, structural optimization methods have been developed to find a balance between cost, structural safety, and material performance. These methods have become a great opportunity in the steel structural engineering domain since they have as their main purpose is weight minimization, a factor directly correlated to the real cost of the structure. Assuming an objective function of minimum weight with stress and displacement constraints provided by Brazilian standards, the present research proposes the sizing optimization and combined approach of sizing and shape optimization, through a software developed to implement the Simulated Annealing metaheuristic algorithm. Therefore, two steel plane frame layouts, each admitting four typical truss geometries, were proposed in order to expose the difference between the optimal solutions. The assessment of the optimal solutions indicates a notable weight reduction, especially in sizing and shape optimization combination, in which the quantity of design variables is increased along with the search space, improving the efficiency of the optimal solutions achieved.

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참고문헌

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