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Optimal Analysis of Cable Force of Bridge Suspenders Based on Intelligent Algorithm

Published: 14 March 2022 Publication History

Abstract

Suspenders are important components of concrete-filled steel tube arch bridges. This paper mainly studies the optimization analysis of bridge suspender cable force based on intelligent algorithm. This paper analyzes and compares various cable force calculation models and formulas, and focuses on the application of ANSYS optimization analysis function to analyze and study the relationship between the force of the suspender and the sensitivity of various parameters of the suspender structure. At the same time, this paper uses the large-scale finite element analysis software MIDAS to carry out the simulation analysis of the construction control of the bridge, and optimizes the analysis of the suspender cable force in each construction stage, so that the internal force, displacement, and cable force of the structure can be satisfied within a certain allowable range in the intermediate state of the bridge structure. In addition, the structure reaches the target state when all the booms are stretched, ensuring the safety of the bridge structure in each construction stage. From the perspective of the distribution of the boom force, the side and middle boom boom forces obtained by the zero displacement method are 29.0% and 18.8% larger than those obtained by the minimum bending energy method. The minimum bending energy method and the minimum bending moment method are calculated. The cable force obtained is relatively uniform. The intelligent algorithm improves the recognition accuracy of the suspension rod cable force, especially to ensure the accurate recognition of the short suspension rod cable force.

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  1. Optimal Analysis of Cable Force of Bridge Suspenders Based on Intelligent Algorithm

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    AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture
    October 2021
    3136 pages
    ISBN:9781450385046
    DOI:10.1145/3495018
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 14 March 2022

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