{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T15:11:22Z","timestamp":1761664282795,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,8,25]],"date-time":"2021-08-25T00:00:00Z","timestamp":1629849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC0800207"],"award-info":[{"award-number":["2016YFC0800207"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Changsha science and technology project","award":["No. kq1907110"],"award-info":[{"award-number":["No. kq1907110"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2013Walker 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.<\/jats:p>","DOI":"10.3390\/rs13173375","type":"journal-article","created":{"date-parts":[[2021,8,25]],"date-time":"2021-08-25T23:25:50Z","timestamp":1629933950000},"page":"3375","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhen","family":"Fang","sequence":"first","affiliation":[{"name":"Key Laboratory for Wind and Bridge Engineering of Hunan Province, Hunan University, Changsha 410082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5246-2846","authenticated-orcid":false,"given":"Jiayong","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Wind and Bridge Engineering of Hunan Province, Hunan University, Changsha 410082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2440-8054","authenticated-orcid":false,"given":"Xiaolin","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shen, N., Chen, L., Liu, J.B., Wang, L., Tao, T.Y., Wu, D.W., and Chen, R.Z. 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