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<p>Metaheuristic methods are optimization techniques designed to find good solutions for complex problems, especially when traditional methods are impractical. These algorithms provide general approaches that can be applied to a wide range of problems, aiming to find solutions in a reasonable amount of time while guaranteeing the best possible outcome. Among these, Evolutionary Algorithms, which include methods like Genetic Algorithms and Evolution Strategy, are particularly popular for their ability to handle optimization tasks and their bio-inspiration aspect. This paper proposes a methodology called Evolution Strategy based Reliability Optimization (ESRO), which integrates Evolution Strategy with reliability constraints defined by a probability of failure associated with the limit state function of the studied structure. The results obtained from the ESRO approach are compared with those from another methodology proposed by the authors, termed Reliability Genetic Algorithm Optimization (RGAO). Two case studies illustrate these methodologies: the first concerns the reliability optimization of a bridge pier subjected to seismic loading based on response spectrum analysis. The reliability optimization problem is formulated considering minimum design requirements, buckling limits, and stress constraints to determine the optimal dimensions for the structural configuration. The second case focuses on optimizing the height of a prestressed bridge deck slab while ensuring its reliability by adhering to prestressing geometrical conditions. This study enhances the understanding of metaheuristic&#xa0;techniques in structural reliability analysis and optimization, providing valuable insights for researchers and engineers in the field.</p>

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Enhanced metaheuristic evolution strategy for reliability based structural optimization of bridges

  • 期刊论文

  • 发表日期：

    2025年1月18日

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  • Hicham Lamouri,
  • Mouna El Mkhalet,
  • Nouzha Lamdouar

摘要

Metaheuristic methods are optimization techniques designed to find good solutions for complex problems, especially when traditional methods are impractical. These algorithms provide general approaches that can be applied to a wide range of problems, aiming to find solutions in a reasonable amount of time while guaranteeing the best possible outcome. Among these, Evolutionary Algorithms, which include methods like Genetic Algorithms and Evolution Strategy, are particularly popular for their ability to handle optimization tasks and their bio-inspiration aspect. This paper proposes a methodology called Evolution Strategy based Reliability Optimization (ESRO), which integrates Evolution Strategy with reliability constraints defined by a probability of failure associated with the limit state function of the studied structure. The results obtained from the ESRO approach are compared with those from another methodology proposed by the authors, termed Reliability Genetic Algorithm Optimization (RGAO). Two case studies illustrate these methodologies: the first concerns the reliability optimization of a bridge pier subjected to seismic loading based on response spectrum analysis. The reliability optimization problem is formulated considering minimum design requirements, buckling limits, and stress constraints to determine the optimal dimensions for the structural configuration. The second case focuses on optimizing the height of a prestressed bridge deck slab while ensuring its reliability by adhering to prestressing geometrical conditions. This study enhances the understanding of metaheuristic techniques in structural reliability analysis and optimization, providing valuable insights for researchers and engineers in the field.