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Published March 2017 | metadata_only
Journal Article

Identification of time-varying cable tension forces based on adaptive sparse time-frequency analysis of cable vibrations


For cable bridges, the cable tension force plays a crucial role in their construction, assessment and long-term structural health monitoring. Cable tension forces vary in real time with the change of the moving vehicle loads and environmental effects, and this continual variation in tension force may cause fatigue damage of a cable. Traditional vibration-based cable tension force estimation methods can only obtain the time-averaged cable tension force and not the instantaneous force. This paper proposes a new approach to identify the time-varying cable tension forces of bridges based on an adaptive sparse time-frequency analysis method. This is a recently developed method to estimate the instantaneous frequency by looking for the sparsest time-frequency representation of the signal within the largest possible time-frequency dictionary (i.e. set of expansion functions). In the proposed approach, first, the time-varying modal frequencies are identified from acceleration measurements on the cable, then, the time-varying cable tension is obtained from the relation between this force and the identified frequencies. By considering the integer ratios of the different modal frequencies to the fundamental frequency of the cable, the proposed algorithm is further improved to increase its robustness to measurement noise. A cable experiment is implemented to illustrate the validity of the proposed method. For comparison, the Hilbert–Huang transform is also employed to identify the time-varying frequencies, which are then used to calculate the time-varying cable-tension force. The results show that the adaptive sparse time-frequency analysis method produces more accurate estimates of the time-varying cable tension forces than the Hilbert–Huang transform method.

Additional Information

© 2016 John Wiley & Sons, Ltd. Received 26 May 2015; Revised 20 March 2016; Accepted 1 May 2016. One of the authors (Yuequan Bao) acknowledges the support provided by the China Scholarship Council while he was a Visiting Associate at the California Institute of Technology. This research was also supported by grants from the National Basic Research Program of China (Grant No.2013CB036305), the National Natural Science Foundation of China (Grant No. 51378154, 51161120359), which supported the first and fourth authors (Yuequan Bao and Hui Li). The research of Zuoqiang Shi was supported by National Natural Science Foundation of China (Grant No. 11371220). The research of Thomas Hou and Zuoqiang Shi was also in part supported by National Science Foundation of USA (Grant DMS-1318377).

Additional details

August 22, 2023
August 22, 2023