Damage Localization Method for Bridge Group Structures Based on Distributed Strain Data Fusion

CAO Jianxin; WANG Yongliang; DU Jixin; HUANG Yongliang; XIE Hao; LI Chen; WANG Kai

doi:10.13409/j.cnki.jdpme.20231101002

您当前的位置：

首页 >

文章列表页 >

Damage Localization Method for Bridge Group Structures Based on Distributed Strain Data Fusion

更新时间：2025-07-07

- Damage Localization Method for Bridge Group Structures Based on Distributed Strain Data Fusion
- Journal of Disaster Prevention and Mitigation Engineering Vol. 45, Issue 3, Pages: 664-671(2025)
- 作者机构：
  
  1.山东铁路投资控股集团有限公司高速铁路工程技术研发中心，山东济南 250102
  2.山东轨道交通研究院有限公司，山东济南 250014
  3.济南轨道交通集团有限公司，山东济南 250014
  4.济南黄河路桥建设集团有限公司，山东济南 250014
- 作者简介：
- 基金信息：
- DOI：10.13409/j.cnki.jdpme.20231101002
  CLC： U446
- Received：01 November 2023，
  
  Revised：2024-03-05，
  
  Published：28 June 2025
- 稿件说明：
移动端阅览
曹建新,王永亮,杜继新等.基于分布式应变数据融合的桥群结构损伤定位方法[J].防灾减灾工程学报,2025,45(03):664-671.

CAO Jianxin,WANG Yongliang,DU Jixin,et al.Damage Localization Method for Bridge Group Structures Based on Distributed Strain Data Fusion[J].Journal of Disaster Prevention and Mitigation Engineering,2025,45(03):664-671.
曹建新,王永亮,杜继新等.基于分布式应变数据融合的桥群结构损伤定位方法[J].防灾减灾工程学报,2025,45(03):664-671. DOI： 10.13409/j.cnki.jdpme.20231101002.

CAO Jianxin,WANG Yongliang,DU Jixin,et al.Damage Localization Method for Bridge Group Structures Based on Distributed Strain Data Fusion[J].Journal of Disaster Prevention and Mitigation Engineering,2025,45(03):664-671. DOI： 10.13409/j.cnki.jdpme.20231101002.

摘要

既有中小跨径桥群结构健康监测系统广泛存在健康状态数据缺乏、数据受耦合作用影响、测点密度不足等问题，难以对桥群结构的长期运营安全进行精准诊断及评估。针对集群化监测桥梁的结构损伤定位难题，提出了基于分布式应变数据融合的桥群结构损伤定位方法。首先，考虑车体重量、车辆行驶横向位置的不同，理论推导移动荷载作用下简支梁桥的应变时程曲线最大值方程；其次，通过分析桥群内不同桥梁各监测分位点处应变时程曲线最大值的构成特征，构建了基于多桥分布式应变监测数据融合的结构损伤定位指标；最后，提出桥群结构分布式测点损伤定位指标的归一化方法，探讨了归一化损伤定位指标与主梁结构损伤的关系，实现桥群内全部桥梁结构的损伤定位。与传统方法不同，所提方法利用连续的桥群结构承受车辆荷载基本一致的特点，通过融合多座桥梁的监测数据，挖掘出桥梁之间应变监测数据存在的耦联关系，有效提高了桥梁结构损伤定位的精度。结果表明：该方法可以实现在桥梁结构单元3%的刚度衰减和15%测试噪声影响下的损伤定位，特别适用于长距离实际桥群工程的结构损伤快速定位。

Abstract

Existing structural health monitoring systems for small- and medium-span bridge groups often suffer from issues such as insufficient health status data

data coupling effects

and low sensor density

making it challenging to accurately diagnose and assess the long-term operational safety of bridge groups. To address the challenge of structural damage localization in cluster-monitored bridge groups

this paper proposed a method based on distributed strain data fusion. Firstly

considering the variations in vehicle weight and lateral position of vehicle travel

a theoretical equation was derived for the maximum value of the strain time-history curve of a simply supported beam bridge under moving loads. Secondly

by analyzing the compositional characteristics of the maximum values in the strain time-history curves at different monitoring positions across various bridges

a structural damage localization index was constructed based on the fusion of distributed strain monitoring data from multiple bridges. Finally

a normalization method for distributed damage localization indices was proposed

and the relationship between the normalized indices and structural damage of the main girder was explored

enabling damage localization across all bridges in the group. Unlike traditional methods

the proposed approach leverages the consistent vehicle load conditions experienced by continuously arranged bridge structures and utilizes the coupling relationships within the strain monitoring data across bridges to significantly improve the accuracy of structural damage localization. The results show that this method can localize damage under a 3% stiffness reduction of structural components and 15% measurement noise

and is particularly effective for rapid damage localization in long-distance

practical bridge group projects.

关键词

Keywords

references

项长生，党聪，周宇，等 . 基于铰缝损伤度的空心板桥铰缝损伤识别方法［J］. 防灾减灾工程学报， 2022 ， 22 （ 1 ）： 313 ‑ 323 .

Xiang C S ， Dang C ， Zhou Y ， et al . Hinge joint damage identification method of hollow slab bridge based on hinge joint damage degree ［J］. Journal of Disaster Prevention and Mitigation Engineering ， 2022 ， 22 （ 1 ）： 313 ‑ 323 . （in Chinese）

Sun L M ， Shang Z Q ， Xia Y ， et al . Review of bridge structural health monitoring aided by big data and artificial intelligence： from condition assessment to damage detection ［J］. Journal of Structural Engineering ， 2020 ， 146 （ 5 ）： 4020073 .

杨书仁，丁松 . 基于应变比的桥梁损伤识别与评估方法［J］. 公路交通科技， 2021 ， 38 （ 1 ）： 87 ‑ 111 .

Yang S R ， Ding S . A method for bridge damage identification and evaluation based on strain ratio ［J］. Journal of Highway and Transportation Research and Development ， 2021 ， 38 （ 1 ）： 87 ‑ 111 . （in Chinese）

俞鹏，彭卫，王银辉，等 . 基于马氏距离累积量的斜拉桥主梁损伤识别方法［J］. 科学技术与工程， 2018 ， 18 （ 12 ）： 299 ‑ 304 .

Yu P ， Peng W ， Wang Y H ， et al . Structural damage detection for the main girder of cable‑staved bridges based on mahalanobis distance cumulants ［J］. Science Technology and Engineering ， 2018 ， 18 （ 12 ）： 299 ‑ 304 . （in Chinese）

Anastasopoulos D ， De S M ， Vandewalle L ， et al . Damage identification using modal strains identified from operational fiber‑optic Bragg grating data ［J］. Structural Health Minitoring ， 2018 ， 17 （ 6 ）： 1441 ‑ 1459 .

Sazonov E ， Klinkhachorn P . Optimal spatial sampling interval for damage detection by curvature or strain energy mode shapes ［J］. Journal of Sound and Vibration ， 2005 ， 285 （ 4‑5 ）： 783 ‑ 801 .

Chen Z W ， Zhu S Y ， Xu Y L . Damage detection in long suspension bridges using stress influence lines ［J］. Journal of Bridge Engineering ， 2015 ， 20 （ 3 ）： 05014013 .

Wu B T ， Wu G ， Yang C Q ， et al . Damage identification method for continuous girder bridges based on spatially‑distributed long‑gauge strain sensing under moving loads ［J］. Mechanical Systems and Signal Processing ， 2018 ， 104 ： 415 ‑ 435 .

杨书仁，姚建群，丁松 . 基于应变能量函数的桥梁损伤识别方法［J］. 公路， 2021 ， 66 （ 6 ）： 157 ‑ 165 .

Yang S R ， Yao J Q ， Ding S . Bridge damage identification method based on strain energy function ［J］. Highway ， 2021 ， 66 （ 6 ）： 157 ‑ 165 . （in Chinese）

Sim S H ， Li J ， Jo H ， et al . A wireless smart sensor network for automated monitoring of cable tension ［J］. Smart Materials and Structures ， 2014 ， 23 （ 2 ）： 025006 .

Liu Y ， Li H ， Wang Y L ， et al . Damage detection of tunnel based on the high‑density cross‑sectional curvature obtained using strain data from BOTDA sensors ［J］. Mechanical Systems and Signal Processing ， 2021 ， 158 ： 107728 .

An Y K ， Lim H J ， Kim M K ， et al . Application of local reference‑free damage detection techniques to in situ bridges ［J］. Journal of Structural Engineering ， 2014 ， 140 （ 3 ）： 04013069 .

Santos J P ， Orcesi A D ， Cremona C ， et al . Baseline‑free real‑time assessment of structural changes ［J］. Structure and Infrastructure Engineering ， 2015 ， 11 （ 2 ）： 145 ‑ 161 .

Serker N H M K ， Wu Z S ， Li S Z . A Nonphysics‑based approach for vibrationbased structural health monitoring under changing environmental conditions ［J］. Structural Health Monitoring ， 2010 ， 9 （ 2 ）： 145 ‑ 158 .

Meruane V ， Heylen W . Structural damage assessment under varying temperature conditions ［J］. Structural Health Monitoring ， 2012 ， 11 （ 3 ）： 345 ‑ 357 .

杨婷婷，李岩，林雪琦 . 基于车辆制动激励和小波包能量分析的连续梁桥基础冲刷识别方法［J］. 中国公路学报， 2021 ， 34 （ 2 ）： 51 ‑ 60 .

Yang T T ， Li Y ， Lin X Q . Foundation scour identification method based on vehicle braking excitation and wavelet packet energy analysis for continuous beam bridges ［J］. China Journal of Highway and Transport ， 2021 ， 34 （ 2 ）： 51 ‑ 60 . （in Chinese）

周瑞娇，王艳，陈淮 . 在役大跨径曲弦桁架梁桥车桥耦合振动分析［J］. 振动工程学报， 2022 ， 35 （ 1 ）： 103 ‑ 112 .

Zhou R J ， Wang Y ， Chen H . Analysis of vehicle‑bridge coupled vibration of long‑span curved truss bridge in service ［J］. Journal of Vibration Engineering ， 2022 ， 35 （ 1 ）： 103 ‑ 112 . （in Chinese）

Prateek G V ， Yo‑El J ， Nehorai A . Sparsity‑assisted signal denoising and pattern recognition in time‑series data ［J］. Circuits Systems and Signal Processing ， 2022 ， 41 （ 1 ）： 249 ‑ 298 .

Zheng X ， Yang D H ， Yi T H ， et al . Bridge influence line identification from structural dynamic responses induced by a high‑speed vehicle ［J］. Structural Control & Health Monitoring ， 2020 ， 27 （ 7 ）： e2544 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Rapid Assessment of Post‑earthquake Building Collapse Using Web‑based Disaster Data Fusion

Suspension Identification on Buried Pipeline based on Distributed Strain Monitoring

Related Author

YAN Zezheng

ZHAO Hanping

LIANG Jinfan

ZHU Lei

LIU Yuxiang

LIU Xudu

FENG Xin

LI Minghao

Related Institution

Aerospace Science & Industry Haiying Group Co., Ltd.

Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University

Faculty of Geographical Science, Beijing Normal University

State Key Lab of Coastal & Offshore Engineering, Dalian University of Technology

⁰