Optimization of cable forces for multi-span concrete extradosed bridges based on a multi-objective genetic algorithm

Shixiang Hu; Wengang Ma; Yuqin Zhu; Ling Cong

doi:10.1117/12.3004709

9 October 2023 Optimization of cable forces for multi-span concrete extradosed bridges based on a multi-objective genetic algorithm

Shixiang Hu, Wengang Ma, Yuqin Zhu, Ling Cong

Author Affiliations +

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127910R (2023) https://doi.org/10.1117/12.3004709
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

This paper proposes a method to better address the optimization problem of cable forces in cable-stayed bridges, particularly when considering multiple objectives simultaneously. It applies genetic algorithms to optimize cable forces and utilizes Pareto theory for multi-objective optimization. The proposed method is implemented using MATLAB software as the programming platform and ANSYS parameterized modeling. Additionally, an actual bridge is used as a case study to validate the proposed optimization method. By minimizing the main beam bending strain energy and the maximum cable force of the cable as objectives with cable force values as optimization variables, the internal force state of the structure is improved by adjusting the cable forces distribution. The Pareto optimal solution is obtained through optimization calculations, reflecting the minimum main beam bending energy that can be achieved under different cable force safety factors. This solution can guide engineering design.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shixiang Hu, Wengang Ma, Yuqin Zhu, and Ling Cong "Optimization of cable forces for multi-span concrete extradosed bridges based on a multi-objective genetic algorithm", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127910R (9 October 2023); https://doi.org/10.1117/12.3004709

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