Automatic Modal Frequency Identification of Bridge Cables under Influence of Abnormal Monitoring Data

AbstractAutomatic identification of modal frequencies can be used to directly estimate the real-time tension force of bridge cables and provide early damage alarming. However, a large amount of abnormal monitoring data usually exists in structural health monitoring (SHM) systems. Abnormal monitoring...

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Bibliographic Details
Published in:Journal of performance of constructed facilities 2024-12, Vol.38 (6)
Main Authors: Ju, Hanwen, Deng, Yang, Zhao, Yingjie, Yi, Ting-Hua, Zhong, Guoqiang, Li, Aiqun
Format: Article
Language:English
Subjects:
Accuracy
Cable-stayed bridges
Cables
Clustering
Damage detection
Identification
Identification methods
Radio frequency
Real time
Structural health monitoring
Technical Papers
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Summary:AbstractAutomatic identification of modal frequencies can be used to directly estimate the real-time tension force of bridge cables and provide early damage alarming. However, a large amount of abnormal monitoring data usually exists in structural health monitoring (SHM) systems. Abnormal monitoring data may lead to faulty results of modal frequency identification and incorrect cable tension force estimation. Then, false or missing alarming of cable damage may arise. An automatic identification method of bridge cable modal frequencies under the influence of abnormal monitoring data is proposed in this study. The peak picking (PP) method is used to automatically obtain the original identification results of cable modal frequencies. To remove faulty frequency identification results, a multidimensional density-based clustering model is established. The cable acceleration data of the Waitan cable-stayed bridge are used to verify the accuracy of the proposed method. The influence of various abnormal monitoring data on frequency identification is investigated, and the accuracy of multidimensional clustering models is verified. The results show that abnormal monitoring data have a harmful influence on automatic modal frequency identification for bridge cables. The accuracy of the multidimensional clustering model for faulty frequency identification results is more than 99%. After removing the faulty frequency identification results, the correlation between the cable modal frequencies and environmental temperature becomes clearer and more reasonable.
ISSN:0887-3828
1943-5509
DOI:10.1061/JPCFEV.CFENG-4680