A feasible identification method of uncertainty responses for vehicle structures

Xu, X; Chen, X; Liu, Z; Zhang, Y; Xu, Y; Fang, J; Gao, Y

A feasible identification method of uncertainty responses for vehicle structures

Xu, X Chen, X Liu, Z Zhang, Y Xu, Y Fang, J

Gao, Y

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Structural and Multidisciplinary Optimization, 2021, 64, (6), pp. 3861-3876
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Xu, X
dc.contributor.author	Chen, X
dc.contributor.author	Liu, Z
dc.contributor.author	Zhang, Y
dc.contributor.author	Xu, Y
dc.contributor.author	Fang, J https://orcid.org/0000-0003-0119-6108
dc.contributor.author	Gao, Y
dc.date.accessioned	2022-04-26T00:47:39Z
dc.date.available	2022-04-26T00:47:39Z
dc.date.issued	2021-12-01
dc.identifier.citation	Structural and Multidisciplinary Optimization, 2021, 64, (6), pp. 3861-3876
dc.identifier.issn	1615-147X
dc.identifier.issn	1615-1488
dc.identifier.uri	http://hdl.handle.net/10453/156596
dc.description.abstract	Many unavoidable uncertainties in the engineering structure will affect its performance response. It is necessary to analyze the uncertain responses generated by uncertain factors in the vehicle design. Therefore, this study proposes a feasible identification method of uncertainty responses for vehicle structures. The proposed method consists of radial basis function neural network (RBFNN) and Taylor interval expansion (IE) model. The optimal network parameters of RBFNN are trained by the improved K-means clustering algorithm and singular value decomposition (SVD). Here, the first-order and second-order differential equations are derived from the RBFNN parameters since it is difficult to calculate the partial derivatives of complex systems without explicit expressions. A series of typical functions are tested and the results show that the trained RBFNN parameters can approximate the first-order and second-order partial derivatives of different types of functions. Besides, the subinterval expansion method is further applied to improve the calculation accuracy of uncertain structural responses. Finally, the proposed method is applied to four engineering applications (vehicle hood, mechanical claw, anti-collision structures, and multi-link suspension). Compared with genetic algorithm (GA) and Monte Carlo simulation (MCS), the proposed method can improve computational efficiency while ensuring accuracy. The identification method of interval uncertainty responses can be used as an alternative for uncertainty analysis and subsequent reliability or robustness optimization.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Structural and Multidisciplinary Optimization
dc.relation.isbasedon	10.1007/s00158-021-03065-0
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	01 Mathematical Sciences, 09 Engineering
dc.subject.classification	Design Practice & Management
dc.title	A feasible identification method of uncertainty responses for vehicle structures
dc.type	Journal Article
utslib.citation.volume	64
utslib.for	01 Mathematical 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	open_access	*
utslib.copyright.embargo	2022-12-01T00:00:00+1000Z
dc.date.updated	2022-04-26T00:47:32Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	64
utslib.citation.issue	6

Abstract:

Many unavoidable uncertainties in the engineering structure will affect its performance response. It is necessary to analyze the uncertain responses generated by uncertain factors in the vehicle design. Therefore, this study proposes a feasible identification method of uncertainty responses for vehicle structures. The proposed method consists of radial basis function neural network (RBFNN) and Taylor interval expansion (IE) model. The optimal network parameters of RBFNN are trained by the improved K-means clustering algorithm and singular value decomposition (SVD). Here, the first-order and second-order differential equations are derived from the RBFNN parameters since it is difficult to calculate the partial derivatives of complex systems without explicit expressions. A series of typical functions are tested and the results show that the trained RBFNN parameters can approximate the first-order and second-order partial derivatives of different types of functions. Besides, the subinterval expansion method is further applied to improve the calculation accuracy of uncertain structural responses. Finally, the proposed method is applied to four engineering applications (vehicle hood, mechanical claw, anti-collision structures, and multi-link suspension). Compared with genetic algorithm (GA) and Monte Carlo simulation (MCS), the proposed method can improve computational efficiency while ensuring accuracy. The identification method of interval uncertainty responses can be used as an alternative for uncertainty analysis and subsequent reliability or robustness optimization.

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http://hdl.handle.net/10453/156596