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浙江大学学报(工学版)  2025, Vol. 59 Issue (3): 460-468    DOI: 10.3785/j.issn.1008-973X.2025.03.003
交通工程、土木工程     
基于变分模态分解和分段多项式截断奇异值分解的桥梁影响线识别
万桂军1,2(),黎剑安3,4,冯东明3,4,*()
1. 浙江大学 建筑工程学院,浙江 杭州 310058
2. 中海建筑有限公司,广东 深圳 518055
3. 东南大学 混凝土及预应力混凝土结构教育部重点实验室,江苏 南京 211189
4. 东南大学 土木工程学院,江苏 南京 211189
Bridge influence line identification based on variational mode decomposition and piecewise polynomial truncated singular value decomposition
Guijun WAN1,2(),Jianan LI3,4,Dongming FENG3,4,*()
1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2. China Overseas Construction Limited, Shenzhen 518055, China
3. Key Laboratory of Concrete and Prestressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 211189, China
4. School of Civil Engineering, Southeast University, Nanjing 211189, China
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摘要:

为了提高桥梁影响线的识别精度,提出基于变分模态分解(VMD)和分段多项式截断奇异值分解(PPTSVD)的桥梁影响线识别方法. 该方法应用VMD技术将桥梁位移分解成若干固有模态函数(IMF),通过融合多个低阶IMF提取桥梁响应的准静态成分,利用PPTSVD从准静态成分中识别桥梁影响线. 为了验证所提方法的准确性,建立三跨连续梁桥和四轴车数值仿真模型,模拟不同车速、路面不平度和噪声水平,并针对500组数值仿真结果进行测试. 将所提方法与经典方法进行对比,并全面讨论车速、路面不平度和噪声对识别结果的影响. 进行验证试验,测试实验室环境下所提方法的准确性和适用性. 研究结果表明,所提方法能从桥梁响应中准确识别出桥梁影响线,最大误差仅为1.38%;相比传统方法,所提方法显著减少了车速、路面不平度和噪声对识别结果的干扰,提高了识别的鲁棒性和精度.

关键词: 桥梁健康监测桥梁动力分析车桥耦合系统影响线识别变分模态分解    
Abstract:

A method based on the variational mode decomposition (VMD) and the piecewise polynomial truncated singular value decomposition (PPTSVD) was proposed to improve the accuracy of bridge influence line identification. The method applied VMD to decompose bridge displacement into several intrinsic mode functions (IMFs), extracted the quasi-static component of the bridge response by fusing multiple low-order IMFs, and identified the bridge influence line from the quasi-static component using PPTSVD. To validate the accuracy of the proposed method, a numerical simulation model of a three-span continuous beam bridge and a four-axle vehicle was established, simulating different vehicle speeds, road roughness levels, and noise levels, and tested on 500 sets of numerical results. The proposed method was compared with classical methods, and the effects of vehicle speed, road roughness, and noise on the identification results were comprehensively discussed. Furthermore, validation experiments were conducted to test the accuracy and applicability of the proposed method in a laboratory environment. Results showed that the proposed method accurately identified the bridge influence line from bridge responses, with a maximum error of only 1.38%. Compared to traditional methods, the proposed method significantly reduced the interference of vehicle speed, road roughness, and noise on the identification results, enhancing the robustness and accuracy.

Key words: bridge health monitoring    bridge dynamic analysis    vehicle-bridge interaction system    influence line identification    variational mode decomposition
收稿日期: 2024-01-13 出版日期: 2025-03-10
CLC:  U 441  
基金资助: 东南大学新进教师科研启动经费资助项目(RF1028623149);中央高校基本科研业务费专项资金资助项目(2242024K40013).
通讯作者: 冯东明     E-mail: gjwan@cohl.com;dfeng@seu.edu.cn
作者简介: 万桂军(1984—),男,正高级工程师,博士生,从事桥梁、隧道设计研究. orcid.org/0009-0000-5829-9816. E-mail:gjwan@cohl.com
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引用本文:

万桂军,黎剑安,冯东明. 基于变分模态分解和分段多项式截断奇异值分解的桥梁影响线识别[J]. 浙江大学学报(工学版), 2025, 59(3): 460-468.

Guijun WAN,Jianan LI,Dongming FENG. Bridge influence line identification based on variational mode decomposition and piecewise polynomial truncated singular value decomposition. Journal of ZheJiang University (Engineering Science), 2025, 59(3): 460-468.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.03.003        https://www.zjujournals.com/eng/CN/Y2025/V59/I3/460

图 1  车桥耦合模型示意图
图 2  影响线识别流程图
图 3  桥梁位移的固有模态函数比较
图 4  变分模态分解的截止频率分析
图 5  不同方法识别影响线的结果对比
图 6  不同方法的分析误差对比
图 7  不同路面等级的误差对比
图 8  车速的误差对比
图 9  信噪比的误差对比
图 10  最大误差识别结果(SNR=20)
图 11  影响线识别试验装置
图 12  桥梁位移测量数据
图 13  影响线识别试验结果
测点$\zeta $/%
v =0.1 m/sv =0.3 m/sv =0.5 m/s
D10.440.891.26
D20.561.112.23
表 1  影响线识别误差
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