Time-variant random interval natural frequency analysis of structures

Wu, B; Wu, D; Gao, W; Song, C

Time-variant random interval natural frequency analysis of structures

Wu, B Wu, D

Gao, W Song, C

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Journal of Sound and Vibration, 2018, 414, pp. 284-298
Issue Date:: 2018-02-03

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Field	Value	Language
dc.contributor.author	Wu, B
dc.contributor.author	Wu, D https://orcid.org/0000-0002-7284-5024
dc.contributor.author	Gao, W
dc.contributor.author	Song, C
dc.date.accessioned	2022-09-03T04:05:29Z
dc.date.available	2022-09-03T04:05:29Z
dc.date.issued	2018-02-03
dc.identifier.citation	Journal of Sound and Vibration, 2018, 414, pp. 284-298
dc.identifier.issn	0022-460X
dc.identifier.issn	1095-8568
dc.identifier.uri	http://hdl.handle.net/10453/161256
dc.description.abstract	This paper presents a new robust method namely, unified interval Chebyshev-based random perturbation method, to tackle hybrid random interval structural natural frequency problem. In the proposed approach, random perturbation method is implemented to furnish the statistical features (i.e., mean and standard deviation) and Chebyshev surrogate model strategy is incorporated to formulate the statistical information of natural frequency with regards to the interval inputs. The comprehensive analysis framework combines the superiority of both methods in a way that computational cost is dramatically reduced. This presented method is thus capable of investigating the day-to-day based time-variant natural frequency of structures accurately and efficiently under concrete intrinsic creep effect with probabilistic and interval uncertain variables. The extreme bounds of the mean and standard deviation of natural frequency are captured through the embedded optimization strategy within the analysis procedure. Three particularly motivated numerical examples with progressive relationship in perspective of both structure type and uncertainty variables are demonstrated to justify the computational applicability, accuracy and efficiency of the proposed method.
dc.language	en
dc.publisher	Elsevier BV
dc.relation	http://purl.org/au-research/grants/arc/IH150100006
dc.relation.ispartof	Journal of Sound and Vibration
dc.relation.isbasedon	10.1016/j.jsv.2017.11.009
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	02 Physical Sciences, 09 Engineering
dc.subject.classification	Acoustics
dc.title	Time-variant random interval natural frequency analysis of structures
dc.type	Journal Article
utslib.citation.volume	414
utslib.for	02 Physical Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-09-03T04:05:27Z
pubs.publication-status	Published
pubs.volume	414

Abstract:

This paper presents a new robust method namely, unified interval Chebyshev-based random perturbation method, to tackle hybrid random interval structural natural frequency problem. In the proposed approach, random perturbation method is implemented to furnish the statistical features (i.e., mean and standard deviation) and Chebyshev surrogate model strategy is incorporated to formulate the statistical information of natural frequency with regards to the interval inputs. The comprehensive analysis framework combines the superiority of both methods in a way that computational cost is dramatically reduced. This presented method is thus capable of investigating the day-to-day based time-variant natural frequency of structures accurately and efficiently under concrete intrinsic creep effect with probabilistic and interval uncertain variables. The extreme bounds of the mean and standard deviation of natural frequency are captured through the embedded optimization strategy within the analysis procedure. Three particularly motivated numerical examples with progressive relationship in perspective of both structure type and uncertainty variables are demonstrated to justify the computational applicability, accuracy and efficiency of the proposed method.

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http://hdl.handle.net/10453/161256