Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61048
Title: Operational modal analysis of Canton Tower by a fast frequency domain Bayesian method
Authors: Zhang, FL
Ni, YQ 
Ni, YC
Wang, YW
Keywords: Ambient vibration
Bayesian method
Modal identification
Supertall structure
Uncertainty
Issue Date: 2016
Publisher: Techno Press
Source: Smart structures and systems, 2016, v. 17, no. 2, p. 209-230 How to cite?
Journal: Smart structures and systems 
Abstract: The Canton Tower is a high-rise slender structure with a height of 610 m. A structural health monitoring system has been instrumented on the structure, by which data is continuously monitored. This paper presents an investigation on the identified modal properties of the Canton Tower using ambient vibration data collected during a whole day (24 hours). A recently developed Fast Bayesian FFT method is utilized for operational modal analysis on the basis of the measured acceleration data. The approach views modal identification as an inference problem where probability is used as a measure for the relative plausibility of outcomes given a model of the structure and measured data. Focusing on the first several modes, the modal properties of this supertall slender structure are identified on non-overlapping time windows during the whole day under normal wind speed. With the identified modal parameters and the associated posterior uncertainty, the distribution of the modal parameters in the future is predicted and assessed. By defining the modal root-mean-square value in terms of the power spectral density of modal force identified, the identified natural frequencies and damping ratios versus the vibration amplitude are investigated with the associated posterior uncertainty considered. Meanwhile, the correlations between modal parameters and temperature, modal parameters and wind speed are studied. For comparison purpose, the frequency domain decomposition (FDD) method is also utilized to identify the modal parameters. The identified results obtained by the Bayesian method, the FDD method and a finite element model are compared and discussed.
URI: http://hdl.handle.net/10397/61048
ISSN: 1738-1584
EISSN: 1738-1991
DOI: 10.12989/sss.2016.17.2.209
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