Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory

Quantification of Margins and Uncertainties (QMU) is a decision-support methodology for complex technical decisions centering on performance thresholds and associated margins for engineering systems. Uncertainty propagation is a key element in QMU process for structure reliability analysis at the pr...
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Autor*in:	Chaoyang Xie [verfasserIn] Guijie Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Übergeordnetes Werk:	In: Mathematical Problems in Engineering - Hindawi Limited, 2002, (2016)
Übergeordnetes Werk:	year:2016

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1155/2016/6419058

Katalog-ID:	DOAJ04580723X

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title	Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory
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title_sort	quantification of margins and uncertainties approach for structure analysis based on evidence theory
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title_auth	Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory
abstract	Quantification of Margins and Uncertainties (QMU) is a decision-support methodology for complex technical decisions centering on performance thresholds and associated margins for engineering systems. Uncertainty propagation is a key element in QMU process for structure reliability analysis at the presence of both aleatory uncertainty and epistemic uncertainty. In order to reduce the computational cost of Monte Carlo method, a mixed uncertainty propagation approach is proposed by integrated Kriging surrogate model under the framework of evidence theory for QMU analysis in this paper. The approach is demonstrated by a numerical example to show the effectiveness of the mixed uncertainty propagation method.
abstractGer	Quantification of Margins and Uncertainties (QMU) is a decision-support methodology for complex technical decisions centering on performance thresholds and associated margins for engineering systems. Uncertainty propagation is a key element in QMU process for structure reliability analysis at the presence of both aleatory uncertainty and epistemic uncertainty. In order to reduce the computational cost of Monte Carlo method, a mixed uncertainty propagation approach is proposed by integrated Kriging surrogate model under the framework of evidence theory for QMU analysis in this paper. The approach is demonstrated by a numerical example to show the effectiveness of the mixed uncertainty propagation method.
abstract_unstemmed	Quantification of Margins and Uncertainties (QMU) is a decision-support methodology for complex technical decisions centering on performance thresholds and associated margins for engineering systems. Uncertainty propagation is a key element in QMU process for structure reliability analysis at the presence of both aleatory uncertainty and epistemic uncertainty. In order to reduce the computational cost of Monte Carlo method, a mixed uncertainty propagation approach is proposed by integrated Kriging surrogate model under the framework of evidence theory for QMU analysis in this paper. The approach is demonstrated by a numerical example to show the effectiveness of the mixed uncertainty propagation method.
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title_short	Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory
url	https://doi.org/10.1155/2016/6419058 https://doaj.org/article/4463bae1fd5b451ab59a7b75b5b98b7b http://dx.doi.org/10.1155/2016/6419058 https://doaj.org/toc/1024-123X https://doaj.org/toc/1563-5147
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Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory

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