Hancock_GNSS-aided+accelerometer+frequency+domain+integration+approach+to+monitor+structural+dynamic+displacements+0224FINAL.pdf (1.33 MB)
Download fileGNSS-aided accelerometer frequency domain integration approach to monitor structural dynamic displacements
journal contribution
posted on 2021-11-18, 11:39 authored by Xu Liu, Jian Wang, Jie Zhen, Houzeng Han, Craig HancockCraig HancockThe accelerometer frequency domain integration approach (FDIA) is being actively applied to calculate dynamic displacement responses of large engineering structures. However, it is a relative acceleration measurement as the initial position is unavailable. GNSS offers direct displacement measurements, but has the limitation of relatively low frequency of data compared with alternative measurement techniques. Therefore, this paper proposes an improved FDIA utilising the advantages of GNSS to gain accurate information about the initial position. The performance of the proposed approach is first validated through software simulation. Following the validation, a series of shaking table tests using various vibration frequencies (0.5 HZ, 1 HZ, 1.5 HZ, 2 HZ and 2.5 HZ) are performed at the south square of Beijing University of Civil Engineering and Architecture (BUCEA) using one GNSS receiver and one accelerometer. The results show that the proposed approach can effectively avoid the uncertainty of the initial value and thus enhance the direct measurement accuracy of the dynamic displacements of structures, with root mean square error (RMSE) decreasing from 11.4 mm to 6.8 mm.
Funding
National Natural Science Foundation of China [41874029]
National Key Research and Development Program of China [2016YFC0803103,2020YFD1100201]
History
School
- Architecture, Building and Civil Engineering
Published in
International Journal of Image and Data FusionVolume
12Issue
4Pages
268 - 281Publisher
Taylor & FrancisVersion
- AM (Accepted Manuscript)
Rights holder
© Taylor and FrancisPublisher statement
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Image and Data Fusion on 29 Aug 2021, available online: https://doi.org/10.1080/19479832.2021.1967468Acceptance date
2021-08-09Publication date
2021-08-29Copyright date
2021ISSN
1947-9824eISSN
1947-9824Publisher version
Language
- en