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Bayes-Mode-ID: A Bayesian modal-component-sampling method for operational modal analysis

Yang, Jia-Hua and Lam, Heung-Fai and Beck, James L. (2019) Bayes-Mode-ID: A Bayesian modal-component-sampling method for operational modal analysis. Engineering Structures, 189 . pp. 222-240. ISSN 0141-0296. http://resolver.caltech.edu/CaltechAUTHORS:20190329-083647784

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Abstract

A Bayesian modal-component-sampling system identification (Bayes-Mode-ID) method is developed in this paper. This method can efficiently identify the modal parameters of civil engineering structures under operational conditions even when the number of measured degrees of freedom (DOFs) is large. The mathematical model of the dynamic system is constructed with the modal parameters being the system parameters and the posterior probability density function (PDF) of these modal parameters is formulated using Bayes theorem. Bayesian modal analysis is conducted through generating samples of the modal parameters in the important regions of the posterior PDF. The proposed method can identify the most probable (maximum posterior) values (MPVs) of the modal parameters, together with the corresponding posterior uncertainties based on the generated samples, without assuming an approximate form for the posterior PDF. There are two main difficulties in sampling modal parameters from the posterior PDF. Firstly, it is not possible to analytically normalize the posterior PDF. Secondly, the number of the modal parameters is usually large so the samples cannot be efficiently generated in the important region of the posterior PDF. The proposed component sampling algorithm is tailor made to handle these two problems. This paper covers the theoretical development of the Bayes-Mode-ID for operational modal analysis together with two experimental case studies under laboratory conditions.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.engstruct.2019.03.047DOIArticle
Additional Information:© 2019 Elsevier. Received 11 September 2018, Revised 12 February 2019, Accepted 16 March 2019, Available online 28 March 2019.
Funders:
Funding AgencyGrant Number
National Natural Science Foundation of China51808400
Shanghai Sailing Program18YF1424500
Fundamental Research Funds for the Central Universities22120180007
Research Grants Council of the Hong Kong Special Administrative RegionUNSPECIFIED
Subject Keywords:Operational modal analysis; Bayesian analysis; Modal component sampling
Record Number:CaltechAUTHORS:20190329-083647784
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190329-083647784
Official Citation:Jia-Hua Yang, Heung-Fai Lam, James L. Beck, Bayes-Mode-ID: A Bayesian modal-component-sampling method for operational modal analysis, Engineering Structures, Volume 189, 2019, Pages 222-240, ISSN 0141-0296, https://doi.org/10.1016/j.engstruct.2019.03.047. (http://www.sciencedirect.com/science/article/pii/S0141029618329845)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94286
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:29 Mar 2019 16:09
Last Modified:29 Mar 2019 16:09

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