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...
Ausführliche Beschreibung
Autor*in: |
Chaoyang Xie [verfasserIn] Guijie Li [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Übergeordnetes Werk: |
In: Mathematical Problems in Engineering - Hindawi Limited, 2002, (2016) |
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Übergeordnetes Werk: |
year:2016 |
Links: |
Link aufrufen |
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DOI / URN: |
10.1155/2016/6419058 |
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Katalog-ID: |
DOAJ04580723X |
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10.1155/2016/6419058 doi (DE-627)DOAJ04580723X (DE-599)DOAJ4463bae1fd5b451ab59a7b75b5b98b7b DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Chaoyang Xie verfasserin aut Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Engineering (General). Civil engineering (General) Mathematics Guijie Li verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2016) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2016 https://doi.org/10.1155/2016/6419058 kostenfrei https://doaj.org/article/4463bae1fd5b451ab59a7b75b5b98b7b kostenfrei http://dx.doi.org/10.1155/2016/6419058 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2016 |
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10.1155/2016/6419058 doi (DE-627)DOAJ04580723X (DE-599)DOAJ4463bae1fd5b451ab59a7b75b5b98b7b DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Chaoyang Xie verfasserin aut Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Engineering (General). Civil engineering (General) Mathematics Guijie Li verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2016) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2016 https://doi.org/10.1155/2016/6419058 kostenfrei https://doaj.org/article/4463bae1fd5b451ab59a7b75b5b98b7b kostenfrei http://dx.doi.org/10.1155/2016/6419058 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2016 |
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Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory |
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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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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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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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