Polyphase uncertainty analysis through virtual modelling technique

Wang, Q; Feng, Y; Wu, D; Yang, C; Yu, Y; Li, G; Beer, M; Gao, W

Polyphase uncertainty analysis through virtual modelling technique

Wang, Q Feng, Y Wu, D

Yang, C Yu, Y Li, G Beer, M Gao, W

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Mechanical Systems and Signal Processing, 2022, 162, pp. 108013
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Wang, Q
dc.contributor.author	Feng, Y
dc.contributor.author	Wu, D https://orcid.org/0000-0002-7284-5024
dc.contributor.author	Yang, C
dc.contributor.author	Yu, Y
dc.contributor.author	Li, G
dc.contributor.author	Beer, M
dc.contributor.author	Gao, W
dc.date.accessioned	2023-04-05T04:33:12Z
dc.date.available	2023-04-05T04:33:12Z
dc.date.issued	2022-01-01
dc.identifier.citation	Mechanical Systems and Signal Processing, 2022, 162, pp. 108013
dc.identifier.issn	0888-3270
dc.identifier.issn	1096-1216
dc.identifier.uri	http://hdl.handle.net/10453/169184
dc.description.abstract	A virtual model aided non-deterministic static analysis (including linear and nonlinear analyses) with polyphase uncertainty is presented in this paper. Within an uncertain system, the polyphase uncertainty integrates both probabilistic and non-probabilistic uncertainties, which is more sophisticated than the conventional uncertainty modelling through a single type. To further improve the computational stableness and robustness of the virtual model, a kernel-based machine learning technique, namely Twin Extended Support Vector Regression (T-X-SVR), is newly developed. The feature of auto-learning is fulfilled through the Bayesian optimization. The proposed approach is capable of providing sufficient statistical information, including the membership functions of mean and standard deviation, fuzzy-valued probabilistic density function (PDF) and cumulative distribution function (CDF) for the upper and lower bounds of the concerned structural response. To demonstrate the effectiveness and computational efficiency of the proposed approach, a verification case, where analytical solutions are available, is tested first. Then, two practically stimulated engineering applications are fully investigated.
dc.language	en
dc.publisher	Elsevier
dc.relation	http://purl.org/au-research/grants/arc/IH150100006
dc.relation	http://purl.org/au-research/grants/arc/IH200100010
dc.relation.ispartof	Mechanical Systems and Signal Processing
dc.relation.isbasedon	10.1016/j.ymssp.2021.108013
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 0913 Mechanical Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Acoustics
dc.title	Polyphase uncertainty analysis through virtual modelling technique
dc.type	Journal Article
utslib.citation.volume	162
utslib.for	0905 Civil Engineering
utslib.for	0913 Mechanical Engineering
utslib.for	0915 Interdisciplinary 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	2023-04-05T04:33:10Z
pubs.publication-status	Published
pubs.volume	162

Abstract:

A virtual model aided non-deterministic static analysis (including linear and nonlinear analyses) with polyphase uncertainty is presented in this paper. Within an uncertain system, the polyphase uncertainty integrates both probabilistic and non-probabilistic uncertainties, which is more sophisticated than the conventional uncertainty modelling through a single type. To further improve the computational stableness and robustness of the virtual model, a kernel-based machine learning technique, namely Twin Extended Support Vector Regression (T-X-SVR), is newly developed. The feature of auto-learning is fulfilled through the Bayesian optimization. The proposed approach is capable of providing sufficient statistical information, including the membership functions of mean and standard deviation, fuzzy-valued probabilistic density function (PDF) and cumulative distribution function (CDF) for the upper and lower bounds of the concerned structural response. To demonstrate the effectiveness and computational efficiency of the proposed approach, a verification case, where analytical solutions are available, is tested first. Then, two practically stimulated engineering applications are fully investigated.

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http://hdl.handle.net/10453/169184