A statistical strategy to select optimal structural health monitoring data in operational modal analysis

Wang, F.L., Chan, Tommy H.T., Thambiratnam, David, & Tan, Andy (2013) A statistical strategy to select optimal structural health monitoring data in operational modal analysis. Australian Journal of Structural Engineering, 14(1), pp. 1-12.

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Operational modal analysis (OMA) is prevalent in modal identifi cation of civil structures. It asks for response measurements of the underlying structure under ambient loads. A valid OMA method requires the excitation be white noise in time and space. Although there are numerous applications of OMA in the literature, few have investigated the statistical distribution of a measurement and the infl uence of such randomness to modal identifi cation. This research has attempted modifi ed kurtosis to evaluate the statistical distribution of raw measurement data. In addition, a windowing strategy employing this index has been proposed to select quality datasets. In order to demonstrate how the data selection strategy works, the ambient vibration measurements of a laboratory bridge model and a real cable-stayed bridge have been respectively considered. The analysis incorporated with frequency domain decomposition (FDD) as the target OMA approach for modal identifi cation. The modal identifi cation results using the data segments with different randomness have been compared. The discrepancy in FDD spectra of the results indicates that, in order to fulfi l the assumption of an OMA method, special care shall be taken in processing a long vibration measurement data. The proposed data selection strategy is easy-to-apply and verifi ed effective in modal analysis.

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ID Code: 62326
Item Type: Journal Article
Refereed: Yes
Keywords: Structural health monitoring, operational modal analysis, output-only, damage detection, kurtosis
DOI: 10.7158/S12-030.2013.14.1
ISSN: 13287982
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Institution of Engineers Australia
Deposited On: 05 Sep 2013 22:55
Last Modified: 09 Apr 2014 12:20

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