Abstract
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.
Highlights
Global Navigation Satellite System (GNSS) positioning technology, as an innovative monitoring method, features the provision of real-time 3D absolute displacements of monitoring structures; continuously autonomous operation, regardless of the weather and visibility conditions; and easy operation
When the effective Intrinsic Mode Functions (IMFs) are defined by applying judgment criteria, Hilbert transform (HT) and a nonlinear exponential function are subsequently performed to extract the modal information: natural frequencies (NFs)
On the basis of Liu et al [32], this paper proposes a criterion based on the Pearson correlation coefficient between each decomposed IMF component IMF n
Summary
Global Navigation Satellite System (GNSS) positioning technology, as an innovative monitoring method, features the provision of real-time 3D absolute displacements of monitoring structures; continuously autonomous operation, regardless of the weather and visibility conditions; and easy operation. The GNSS positioning technology can overcome some shortcomings of traditional monitoring methods, as it identifies low-frequency structural vibration responses. Xia et al [27] separated the mono-components from the health monitoring data of the civil structure via scale–space EWT and obtained the instantaneous modal parameters by the FREEVIB method Another way to improve the traditional EWT is optimizing the Fourier spectrum segmentation method. Fewer studies discussed the judgment criterion of effective IMFs among a series of EWT-extracted IMFs. With the continuous development of GNSS hardware and software, it was crucial to identify the structural modes and dynamic displacements from GNSS vibration monitoring data. An improved EWT-based method is presented to denoise data and identify the modal parameters of bridge structures.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
