Technical Papers
Jun 8, 2022

Adaptive Identification of Time-Varying Cable Tension Based on Improved Variational Mode Decomposition

Publication: Journal of Bridge Engineering
Volume 27, Issue 8

Abstract

Stay cables are crucial components in cable-stayed bridges. The time-varying cable tension is an important indicator for the condition assessment of cables because the variation in tension will cause fatigue damage. This paper proposes an adaptive identification method for time-varying cable tension based on an improved variational mode decomposition (VMD) algorithm. In the proposed method, the cable vibration signal is decomposed by improved VMD to identify the time-varying modal frequencies by the Hilbert transform, and then the time-varying cable tension is calculated by the vibration method. To realize adaptive and accurate identification, the VMD algorithm is improved by optimizing parameters: the number of decomposed modes is determined by the characteristic that frequency differences between the adjacent modal frequencies of the cable vibration signal are equal, and the balancing parameter is optimized by constructing an evaluation index and searching for the minimum of the index. A numerical simulation illustrates that the accuracy of the proposed method is satisfactory. To verify the practicability of the proposed method, the monitoring data of a single tower cable-stayed bridge are used. The results demonstrate that the proposed method is a practical and reliable method to identify the time-varying cable tension.

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Acknowledgments

This research work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 51978128 and 52078100).

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Go to Journal of Bridge Engineering
Journal of Bridge Engineering
Volume 27Issue 8August 2022

History

Received: Jan 3, 2021
Accepted: Apr 5, 2022
Published online: Jun 8, 2022
Published in print: Aug 1, 2022
Discussion open until: Nov 8, 2022

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Ph.D. Candidate, School of Civil Engineering, Dalian Univ. of Technology, Dalian 116023, China. ORCID: https://orcid.org/0000-0003-4261-7108. Email: [email protected]
Ting-Hua Yi, M.ASCE [email protected]
Professor, School of Civil Engineering, Dalian Univ. of Technology, Dalian 116023, China (corresponding author). Email: [email protected]
Chun-Xu Qu, M.ASCE [email protected]
Associate Professor, School of Civil Engineering, Dalian Univ. of Technology, Dalian 116023, China. Email: [email protected]
Hong-Nan Li, F.ASCE [email protected]
Professor, School of Civil Engineering, Dalian Univ. of Technology, Dalian 116023, China. Email: [email protected]
Chief Engineer, China Railway Bridge and Tunnel Technologies, Nanjing 210061, China. Email: [email protected]

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Cited by

  • Real-Time Intelligent Prediction Method of Cable’s Fundamental Frequency for Intelligent Maintenance of Cable-Stayed Bridges, Sustainability, 10.3390/su15054086, 15, 5, (4086), (2023).
  • Piecewise-Fitted Formula for Cable Force Identification Considering Bending Stiffness, Sag, and Inclination, Journal of Bridge Engineering, 10.1061/JBENF2.BEENG-6143, 28, 7, (2023).
  • Multiparameter Identification of Bridge Cables Using XGBoost Algorithm, Journal of Bridge Engineering, 10.1061/JBENF2.BEENG-6021, 28, 5, (2023).
  • On real-time estimation of typhoons-induced cable tension of long-span cable-stayed bridges from health monitoring data, Journal of Wind Engineering and Industrial Aerodynamics, 10.1016/j.jweia.2022.105272, 232, (105272), (2023).
  • Structural damage detection based on variational mode decomposition and kernel PCA-based support vector machine, Engineering Structures, 10.1016/j.engstruct.2022.115565, 278, (115565), (2023).

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