An efficient method for reliability analysis under epistemic uncertainty based on evidence theory and support vector regression.
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| Title: | An efficient method for reliability analysis under epistemic uncertainty based on evidence theory and support vector regression. |
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| Authors: | Xiao, Mi1 (AUTHOR), Gao, Liang1 (AUTHOR) gaoliang@mail.hust.edu.cn, Xiong, Haihong1 (AUTHOR), Luo, Zhen2 (AUTHOR) |
| Source: | Journal of Engineering Design. Oct-Dec2015, Vol. 26 Issue 10-12, p340-364. 25p. 3 Color Photographs, 2 Black and White Photographs, 2 Diagrams, 10 Charts, 8 Graphs. |
| Subjects: | Reliability in engineering, Engineering systems research, Approximation theory, Numerical analysis, Computational complexity |
| Abstract: | With a great capability of dealing with epistemic uncertainty, evidence theory has been utilised to conduct reliability analysis for engineering systems recently. Unfortunately, the discontinuous nature of uncertainty quantification using evidence theory incurs a huge computational cost. This paper proposes an efficient method to improve the computational efficiency of evidence theory for reliability analysis. In this method, evidence variables are transformed into random variables. Support vector regression is used to construct the approximation model of the limit-state function. The most probable point (MPP) of the approximate reliability problem with only random variables is searched out. Based on the MPP, the most probable focal element (MPFE) of the original problem with evidence variables is identified. According to the MPFE and the monotonicity of the limit-state function, contributions of some focal elements to belief and plausibility in evidence theory can be judged directly. Hence, the number of focal elements involved in the calculation of extreme values of the limit-state function is reduced. Four numerical examples are utilised to test the performance of the proposed method. Results indicate that the proposed method can reduce the computational cost on reliability analysis under epistemic uncertainty while ensuring the high accuracy of reliability analysis results. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Engineering Design is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 110203720 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An efficient method for reliability analysis under epistemic uncertainty based on evidence theory and support vector regression. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiao%2C+Mi%22">Xiao, Mi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Liang%22">Gao, Liang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gaoliang@mail.hust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xiong%2C+Haihong%22">Xiong, Haihong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Zhen%22">Luo, Zhen</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+Design%22">Journal of Engineering Design</searchLink>. Oct-Dec2015, Vol. 26 Issue 10-12, p340-364. 25p. 3 Color Photographs, 2 Black and White Photographs, 2 Diagrams, 10 Charts, 8 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Reliability+in+engineering%22">Reliability in engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+systems+research%22">Engineering systems research</searchLink><br /><searchLink fieldCode="DE" term="%22Approximation+theory%22">Approximation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With a great capability of dealing with epistemic uncertainty, evidence theory has been utilised to conduct reliability analysis for engineering systems recently. Unfortunately, the discontinuous nature of uncertainty quantification using evidence theory incurs a huge computational cost. This paper proposes an efficient method to improve the computational efficiency of evidence theory for reliability analysis. In this method, evidence variables are transformed into random variables. Support vector regression is used to construct the approximation model of the limit-state function. The most probable point (MPP) of the approximate reliability problem with only random variables is searched out. Based on the MPP, the most probable focal element (MPFE) of the original problem with evidence variables is identified. According to the MPFE and the monotonicity of the limit-state function, contributions of some focal elements to belief and plausibility in evidence theory can be judged directly. Hence, the number of focal elements involved in the calculation of extreme values of the limit-state function is reduced. Four numerical examples are utilised to test the performance of the proposed method. Results indicate that the proposed method can reduce the computational cost on reliability analysis under epistemic uncertainty while ensuring the high accuracy of reliability analysis results. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Engineering Design is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/09544828.2015.1057557 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 340 Subjects: – SubjectFull: Reliability in engineering Type: general – SubjectFull: Engineering systems research Type: general – SubjectFull: Approximation theory Type: general – SubjectFull: Numerical analysis Type: general – SubjectFull: Computational complexity Type: general Titles: – TitleFull: An efficient method for reliability analysis under epistemic uncertainty based on evidence theory and support vector regression. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiao, Mi – PersonEntity: Name: NameFull: Gao, Liang – PersonEntity: Name: NameFull: Xiong, Haihong – PersonEntity: Name: NameFull: Luo, Zhen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct-Dec2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 09544828 Numbering: – Type: volume Value: 26 – Type: issue Value: 10-12 Titles: – TitleFull: Journal of Engineering Design Type: main |
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