基于递归图和BP神经网络的桥梁损伤识别研究
作者:
作者单位:

(1.中铁上海设计院集团有限公司,上海 200000;2.四川公路桥梁建设集团有限公司,四川 成都 610000;3.长沙理工大学 土木工程学院,湖南 长沙 410114)

作者简介:

通讯作者:

杨金易(1996—),男,硕士生,主要从事桥梁结构智能健康监测方面的研究工作。E-mail:476280340@qq.com

中图分类号:

TU312.3

基金项目:

国家自然科学基金资助项目(52078057);湖南省自然科学基金创新研究群体项目(2020JJ1006)


Research on bridge damage identification based on recurrence plot and BP neural network
Author:
Affiliation:

(1. China Railway Shanghai Design Institute Group Co., Ltd., Shanghai 200000, China;2. Sichuan Highway and Bridge Construction Group Co., Ltd., Chengdu 610000, China;3. School of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, China)

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    摘要:

    为研究递归图和多层前馈(BP)神经网络在桥梁损伤识别方面的应用,以某大跨斜拉桥为例,采用ABAQUS有限元软件建立其三维模型,通过动力分析提取该三维模型的加速度曲线并进行递归图处理和BP神经网络分析。研究结果表明:递归图方法能够初步地识别主梁的损伤位置和损伤程度;BP神经网络分析能够精确识别主梁损伤的具体位置和损伤程度值,且识别准确率均大于85.0%。该方法可为类似桥梁工程的损伤识别提供借鉴。

    Abstract:

    To study the damage identification of bridges using recursive graphs and BP neural networks, taking a certain large cable-stayed bridge as an example, a three-dimensional model was established using ABAQUS finite element software. The acceleration curve of this three-dimensional model was extracted through dynamic analysis and subjected to recursive graph processing and BP neural network analysis. The research results indicate that the recursive graph method can preliminarily identify the location and extent of damage to the main beam. The BP neural network analysis can accurately identify the specific location of damage to the main beam and the degree of damage, with an identification accuracy above 85.0%. This method can provide a reference for damage identification in similar bridge engineering projects.

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引用本文

杨金易,孙兵,岳晓沛,等.基于递归图和BP神经网络的桥梁损伤识别研究[J].交通科学与工程,2024,40(2):116-126.
YANG Jinyi, SUN Bing, YUE Xiaopei, et al. Research on bridge damage identification based on recurrence plot and BP neural network[J]. Journal of Transport Science and Engineering,2024,40(2):116-126.

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  • 收稿日期:2021-12-27
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  • 在线发布日期: 2024-04-29
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