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Article

Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform

1
Key Laboratory for Wind and Bridge Engineering of Hunan Province, Hunan University, Changsha 410082, China
2
College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
*
Author to whom correspondence should be addressed.
Academic Editor: Nicola Cenni
Remote Sens. 2021, 13(17), 3375; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173375
Received: 9 July 2021 / Revised: 19 August 2021 / Accepted: 23 August 2021 / Published: 25 August 2021
It is difficult to accurately identify the dynamic deformation of bridges from Global Navigation Satellite System (GNSS) due to the influence of the multipath effect and random errors, etc. To solve this problem, an improved empirical wavelet transform (EWT)-based procedure was proposed to denoise GNSS data and identify the modal parameters of bridge structures. Firstly, the Yule–Walker algorithm-based auto-power spectrum and Fourier spectrum were jointly adopted to segment the frequency bands of structural dynamic response data. Secondly, the improved EWT algorithm was used to decompose and reconstruct the dynamic response data according to a correlation coefficient-based criterion. Finally, Natural Excitation Technique (NExT) and Hilbert Transform (HT) were applied to identify the modal parameters of structures from the decomposed efficient components. Two groups of simulation data were used to validate the feasibility and reliability of the proposed method, which consisted of the vibration responses of a four-storey steel frame model, and the acceleration response data of a suspension bridge. Moreover, field experiments were carried out on the Wilford suspension bridge in Nottingham, UK, with GNSS and an accelerometer. The fundamental frequency (1.6707 Hz), the damping ratio (0.82%), as well as the maximum dynamic displacements (10.10 mm) of the Wilford suspension bridge were detected by using this proposed method from the GNSS measurements, which were consistent with the accelerometer results. In conclusion, the analysis revealed that the improved EWT-based method was capable of accurately identifying the low-order, closely spaced modal parameters of bridge structures under operational conditions. View Full-Text
Keywords: Global Navigation Satellite System; empirical wavelet transform; modal parameters identification; data denoising Global Navigation Satellite System; empirical wavelet transform; modal parameters identification; data denoising
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MDPI and ACS Style

Fang, Z.; Yu, J.; Meng, X. Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform. Remote Sens. 2021, 13, 3375. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173375

AMA Style

Fang Z, Yu J, Meng X. Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform. Remote Sensing. 2021; 13(17):3375. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173375

Chicago/Turabian Style

Fang, Zhen, Jiayong Yu, and Xiaolin Meng. 2021. "Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform" Remote Sensing 13, no. 17: 3375. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173375

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