Vibration frequency identification method of bridge cable considering abnormal monitoring data

被引：0

作者：

Zhong G. ^{[1
]}

Liu S. ^{[1
]}

Xu R. ^{[1
]}

Ding Y. ^{[2
]}

Song J. ^{[1
]}

Ju H. ^{[3
]}

Deng Y. ^{[3
]}

机构：

[1] Shandong Provincial Communications Planning and Design Institute Group Co. Ltd., Jinan

[2] School of Civil Engineering, Southeast University, Nanjing

[3] School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing

来源：

Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2023年 / 54卷 / 12期

基金：

中国国家自然科学基金;

关键词：

abnormal data; cable; density clustering; frequency identification; structural health monitoring;

D O I：

10.11817/j.issn.1672-7207.2023.12.024

中图分类号：

学科分类号：

摘要：

Aiming at the phenomenon that the bridge health monitoring systems contain a large number of abnormal monitoring data, an identification method for vibration frequencies of bridge cables with the influence of abnormal monitoring data was proposed. Firstly, the approximate band interval of each vibration frequency of the cables was determined according to the distribution characteristics of power spectral density function of normal monitoring data. Peak picking method was adopted to extract the initial identified results of the vibration frequencies of bridge cables automatically in the approximate band intervals. Secondly, a three-dimensional spatial density clustering model was established based on the first three order frequencies. Then, the abnormal values were detected and eliminated from the initial identified results of the vibration frequencies by using the clustering model. The proposed method was verified by using the cable acceleration monitoring data of the Waitan Bridge. The influence of different abnormal monitoring data on identification results of cable frequencies was analyzed. And the detection accuracy of density clustering models with different dimensions on abnormal values of the identified cable frequencies was also investigated. The results show that frequency identification of bridge cables is seriously interfered by abnormal monitoring data. The detection accuracy of three-dimensional spatial density clustering model on the abnormal identified frequencies is more than 98%. In addition, the cable frequency after removing the abnormal identification values shows a reasonable correlation with the ambient temperature. © 2023 Central South University of Technology. All rights reserved.

引用

页码：4870 / 4881

页数：11

共 26 条

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