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|Title:||Understanding and managing identification uncertainty of close modes in operational modal analysis||Authors:||Au, Siu-Kui
Brownjohn, James M. W.
|Keywords:||Engineering::Civil engineering||Issue Date:||2021||Source:||Au, S.-K., Brownjohn, J. M. W., Li, B., & Raby, A. (2020). Understanding and managing identification uncertainty of close modes in operational modal analysis. Mechanical Systems and Signal Processing, 147, 107018-. doi:10.1016/j.ymssp.2020.107016||Project:||EP/N017897/1
|Journal:||Mechanical Systems and Signal Processing||Abstract:||Close modes are much more difficult to identify than well-separated modes and their identification (ID) results often have significantly larger uncertainty or variability. The situation becomes even more challenging in operational modal analysis (OMA), which is currently the most economically viable means for obtaining in-situ dynamic properties of large civil structures and where ID uncertainty management is most needed. To understand ID uncertainty and manage it in field test planning, this work develops the ‘uncertainty law’ for close modes, i.e., closed form analytical expressions for the remaining uncertainty of modal parameters identified using output-only ambient vibration data. The expressions reveal a fundamental definition that quantifies ‘how close is close’ and demystify the roles of various governing factors. The results are verified with synthetic, laboratory and field data. Statistics of governing factors from field data reveal OMA challenges in different situations, now accountable within a coherent probabilistic framework. Recommendations are made for planning ambient vibration tests taking close modes into account. Up to modelling assumptions and the use of probability, the uncertainty law dictates the achievable precision of modal properties regardless of the ID algorithm used. The mathematical theory behind the results in this paper is presented in a companion paper.||URI:||https://hdl.handle.net/10356/143940||ISSN:||0888-3270||DOI:||10.1016/j.ymssp.2020.107018||Rights:||© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||CEE Journal Articles|
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