An innovative adaptive Kriging-based parallel system reliability method under error stopping criterion for efficiently analyzing the global reliability sensitivity index
Abstract Global reliability sensitivity index measures the effect of uncertainty of model input on model failure probability, which is critical for simplifying analysis model and the reliability-based design optimization model. For efficiently estimating the global reliability sensitivity index of e...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yun, Wanying [verfasserIn] Zhang, Shutong [verfasserIn] Li, Fengyuan [verfasserIn] Chen, Xiangming [verfasserIn] Wang, Zhe [verfasserIn] Feng, Kaixuan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
Global reliability sensitivity index Single-loop estimation process |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Structural and multidisciplinary optimization - Springer Berlin Heidelberg, 1989, 67(2024), 4 vom: 13. März |
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| Übergeordnetes Werk: |
volume:67 ; year:2024 ; number:4 ; day:13 ; month:03 |
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| DOI / URN: |
10.1007/s00158-024-03752-8 |
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| Katalog-ID: |
SPR055126103 |
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| 520 | |a Abstract Global reliability sensitivity index measures the effect of uncertainty of model input on model failure probability, which is critical for simplifying analysis model and the reliability-based design optimization model. For efficiently estimating the global reliability sensitivity index of each model input, this paper transforms it into estimating an unconditional failure probability and a two failure modes-based parallel system failure probability from the perspective of single-loop estimation method. Furthermore, the relationship of computational accuracy among the global reliability sensitivity index, the unconditional failure probability, and the two failure modes-based system failure probability is constructed, on which the error stopping criterion-based sequentially adaptive Kriging model approach is developed to significantly decrease the number of calls to the actual limit state functions and the corresponding computational time under the sufficient accuracy. Results of three case studies covering explicit and implicit limit state functions demonstrate the accuracy and efficiency of the proposed method. | ||
| 650 | 4 | |a Global reliability sensitivity index |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Single-loop estimation process |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sequential adaptive Kriging model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Error propagation analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Error stopping criterion |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Zhang, Shutong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Fengyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Xiangming |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Zhe |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Kaixuan |e verfasserin |4 aut | |
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10.1007/s00158-024-03752-8 doi (DE-627)SPR055126103 (SPR)s00158-024-03752-8-e DE-627 ger DE-627 rakwb eng 510 VZ 50.03 bkl Yun, Wanying verfasserin aut An innovative adaptive Kriging-based parallel system reliability method under error stopping criterion for efficiently analyzing the global reliability sensitivity index 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global reliability sensitivity index measures the effect of uncertainty of model input on model failure probability, which is critical for simplifying analysis model and the reliability-based design optimization model. For efficiently estimating the global reliability sensitivity index of each model input, this paper transforms it into estimating an unconditional failure probability and a two failure modes-based parallel system failure probability from the perspective of single-loop estimation method. Furthermore, the relationship of computational accuracy among the global reliability sensitivity index, the unconditional failure probability, and the two failure modes-based system failure probability is constructed, on which the error stopping criterion-based sequentially adaptive Kriging model approach is developed to significantly decrease the number of calls to the actual limit state functions and the corresponding computational time under the sufficient accuracy. Results of three case studies covering explicit and implicit limit state functions demonstrate the accuracy and efficiency of the proposed method. Global reliability sensitivity index (dpeaa)DE-He213 Single-loop estimation process (dpeaa)DE-He213 Sequential adaptive Kriging model (dpeaa)DE-He213 Error propagation analysis (dpeaa)DE-He213 Error stopping criterion (dpeaa)DE-He213 Zhang, Shutong verfasserin aut Li, Fengyuan verfasserin aut Chen, Xiangming verfasserin aut Wang, Zhe verfasserin aut Feng, Kaixuan verfasserin aut Enthalten in Structural and multidisciplinary optimization Springer Berlin Heidelberg, 1989 67(2024), 4 vom: 13. März (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:67 year:2024 number:4 day:13 month:03 https://dx.doi.org/10.1007/s00158-024-03752-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.03 VZ AR 67 2024 4 13 03 |
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10.1007/s00158-024-03752-8 doi (DE-627)SPR055126103 (SPR)s00158-024-03752-8-e DE-627 ger DE-627 rakwb eng 510 VZ 50.03 bkl Yun, Wanying verfasserin aut An innovative adaptive Kriging-based parallel system reliability method under error stopping criterion for efficiently analyzing the global reliability sensitivity index 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global reliability sensitivity index measures the effect of uncertainty of model input on model failure probability, which is critical for simplifying analysis model and the reliability-based design optimization model. For efficiently estimating the global reliability sensitivity index of each model input, this paper transforms it into estimating an unconditional failure probability and a two failure modes-based parallel system failure probability from the perspective of single-loop estimation method. Furthermore, the relationship of computational accuracy among the global reliability sensitivity index, the unconditional failure probability, and the two failure modes-based system failure probability is constructed, on which the error stopping criterion-based sequentially adaptive Kriging model approach is developed to significantly decrease the number of calls to the actual limit state functions and the corresponding computational time under the sufficient accuracy. Results of three case studies covering explicit and implicit limit state functions demonstrate the accuracy and efficiency of the proposed method. Global reliability sensitivity index (dpeaa)DE-He213 Single-loop estimation process (dpeaa)DE-He213 Sequential adaptive Kriging model (dpeaa)DE-He213 Error propagation analysis (dpeaa)DE-He213 Error stopping criterion (dpeaa)DE-He213 Zhang, Shutong verfasserin aut Li, Fengyuan verfasserin aut Chen, Xiangming verfasserin aut Wang, Zhe verfasserin aut Feng, Kaixuan verfasserin aut Enthalten in Structural and multidisciplinary optimization Springer Berlin Heidelberg, 1989 67(2024), 4 vom: 13. März (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:67 year:2024 number:4 day:13 month:03 https://dx.doi.org/10.1007/s00158-024-03752-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.03 VZ AR 67 2024 4 13 03 |
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10.1007/s00158-024-03752-8 doi (DE-627)SPR055126103 (SPR)s00158-024-03752-8-e DE-627 ger DE-627 rakwb eng 510 VZ 50.03 bkl Yun, Wanying verfasserin aut An innovative adaptive Kriging-based parallel system reliability method under error stopping criterion for efficiently analyzing the global reliability sensitivity index 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global reliability sensitivity index measures the effect of uncertainty of model input on model failure probability, which is critical for simplifying analysis model and the reliability-based design optimization model. For efficiently estimating the global reliability sensitivity index of each model input, this paper transforms it into estimating an unconditional failure probability and a two failure modes-based parallel system failure probability from the perspective of single-loop estimation method. Furthermore, the relationship of computational accuracy among the global reliability sensitivity index, the unconditional failure probability, and the two failure modes-based system failure probability is constructed, on which the error stopping criterion-based sequentially adaptive Kriging model approach is developed to significantly decrease the number of calls to the actual limit state functions and the corresponding computational time under the sufficient accuracy. Results of three case studies covering explicit and implicit limit state functions demonstrate the accuracy and efficiency of the proposed method. Global reliability sensitivity index (dpeaa)DE-He213 Single-loop estimation process (dpeaa)DE-He213 Sequential adaptive Kriging model (dpeaa)DE-He213 Error propagation analysis (dpeaa)DE-He213 Error stopping criterion (dpeaa)DE-He213 Zhang, Shutong verfasserin aut Li, Fengyuan verfasserin aut Chen, Xiangming verfasserin aut Wang, Zhe verfasserin aut Feng, Kaixuan verfasserin aut Enthalten in Structural and multidisciplinary optimization Springer Berlin Heidelberg, 1989 67(2024), 4 vom: 13. März (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:67 year:2024 number:4 day:13 month:03 https://dx.doi.org/10.1007/s00158-024-03752-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.03 VZ AR 67 2024 4 13 03 |
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10.1007/s00158-024-03752-8 doi (DE-627)SPR055126103 (SPR)s00158-024-03752-8-e DE-627 ger DE-627 rakwb eng 510 VZ 50.03 bkl Yun, Wanying verfasserin aut An innovative adaptive Kriging-based parallel system reliability method under error stopping criterion for efficiently analyzing the global reliability sensitivity index 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global reliability sensitivity index measures the effect of uncertainty of model input on model failure probability, which is critical for simplifying analysis model and the reliability-based design optimization model. For efficiently estimating the global reliability sensitivity index of each model input, this paper transforms it into estimating an unconditional failure probability and a two failure modes-based parallel system failure probability from the perspective of single-loop estimation method. Furthermore, the relationship of computational accuracy among the global reliability sensitivity index, the unconditional failure probability, and the two failure modes-based system failure probability is constructed, on which the error stopping criterion-based sequentially adaptive Kriging model approach is developed to significantly decrease the number of calls to the actual limit state functions and the corresponding computational time under the sufficient accuracy. Results of three case studies covering explicit and implicit limit state functions demonstrate the accuracy and efficiency of the proposed method. Global reliability sensitivity index (dpeaa)DE-He213 Single-loop estimation process (dpeaa)DE-He213 Sequential adaptive Kriging model (dpeaa)DE-He213 Error propagation analysis (dpeaa)DE-He213 Error stopping criterion (dpeaa)DE-He213 Zhang, Shutong verfasserin aut Li, Fengyuan verfasserin aut Chen, Xiangming verfasserin aut Wang, Zhe verfasserin aut Feng, Kaixuan verfasserin aut Enthalten in Structural and multidisciplinary optimization Springer Berlin Heidelberg, 1989 67(2024), 4 vom: 13. März (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:67 year:2024 number:4 day:13 month:03 https://dx.doi.org/10.1007/s00158-024-03752-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.03 VZ AR 67 2024 4 13 03 |
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10.1007/s00158-024-03752-8 doi (DE-627)SPR055126103 (SPR)s00158-024-03752-8-e DE-627 ger DE-627 rakwb eng 510 VZ 50.03 bkl Yun, Wanying verfasserin aut An innovative adaptive Kriging-based parallel system reliability method under error stopping criterion for efficiently analyzing the global reliability sensitivity index 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Global reliability sensitivity index measures the effect of uncertainty of model input on model failure probability, which is critical for simplifying analysis model and the reliability-based design optimization model. For efficiently estimating the global reliability sensitivity index of each model input, this paper transforms it into estimating an unconditional failure probability and a two failure modes-based parallel system failure probability from the perspective of single-loop estimation method. Furthermore, the relationship of computational accuracy among the global reliability sensitivity index, the unconditional failure probability, and the two failure modes-based system failure probability is constructed, on which the error stopping criterion-based sequentially adaptive Kriging model approach is developed to significantly decrease the number of calls to the actual limit state functions and the corresponding computational time under the sufficient accuracy. Results of three case studies covering explicit and implicit limit state functions demonstrate the accuracy and efficiency of the proposed method. Global reliability sensitivity index (dpeaa)DE-He213 Single-loop estimation process (dpeaa)DE-He213 Sequential adaptive Kriging model (dpeaa)DE-He213 Error propagation analysis (dpeaa)DE-He213 Error stopping criterion (dpeaa)DE-He213 Zhang, Shutong verfasserin aut Li, Fengyuan verfasserin aut Chen, Xiangming verfasserin aut Wang, Zhe verfasserin aut Feng, Kaixuan verfasserin aut Enthalten in Structural and multidisciplinary optimization Springer Berlin Heidelberg, 1989 67(2024), 4 vom: 13. März (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:67 year:2024 number:4 day:13 month:03 https://dx.doi.org/10.1007/s00158-024-03752-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.03 VZ AR 67 2024 4 13 03 |
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Yun, Wanying @@aut@@ Zhang, Shutong @@aut@@ Li, Fengyuan @@aut@@ Chen, Xiangming @@aut@@ Wang, Zhe @@aut@@ Feng, Kaixuan @@aut@@ |
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an innovative adaptive kriging-based parallel system reliability method under error stopping criterion for efficiently analyzing the global reliability sensitivity index |
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An innovative adaptive Kriging-based parallel system reliability method under error stopping criterion for efficiently analyzing the global reliability sensitivity index |
| abstract |
Abstract Global reliability sensitivity index measures the effect of uncertainty of model input on model failure probability, which is critical for simplifying analysis model and the reliability-based design optimization model. For efficiently estimating the global reliability sensitivity index of each model input, this paper transforms it into estimating an unconditional failure probability and a two failure modes-based parallel system failure probability from the perspective of single-loop estimation method. Furthermore, the relationship of computational accuracy among the global reliability sensitivity index, the unconditional failure probability, and the two failure modes-based system failure probability is constructed, on which the error stopping criterion-based sequentially adaptive Kriging model approach is developed to significantly decrease the number of calls to the actual limit state functions and the corresponding computational time under the sufficient accuracy. Results of three case studies covering explicit and implicit limit state functions demonstrate the accuracy and efficiency of the proposed method. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Global reliability sensitivity index measures the effect of uncertainty of model input on model failure probability, which is critical for simplifying analysis model and the reliability-based design optimization model. For efficiently estimating the global reliability sensitivity index of each model input, this paper transforms it into estimating an unconditional failure probability and a two failure modes-based parallel system failure probability from the perspective of single-loop estimation method. Furthermore, the relationship of computational accuracy among the global reliability sensitivity index, the unconditional failure probability, and the two failure modes-based system failure probability is constructed, on which the error stopping criterion-based sequentially adaptive Kriging model approach is developed to significantly decrease the number of calls to the actual limit state functions and the corresponding computational time under the sufficient accuracy. Results of three case studies covering explicit and implicit limit state functions demonstrate the accuracy and efficiency of the proposed method. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Global reliability sensitivity index measures the effect of uncertainty of model input on model failure probability, which is critical for simplifying analysis model and the reliability-based design optimization model. For efficiently estimating the global reliability sensitivity index of each model input, this paper transforms it into estimating an unconditional failure probability and a two failure modes-based parallel system failure probability from the perspective of single-loop estimation method. Furthermore, the relationship of computational accuracy among the global reliability sensitivity index, the unconditional failure probability, and the two failure modes-based system failure probability is constructed, on which the error stopping criterion-based sequentially adaptive Kriging model approach is developed to significantly decrease the number of calls to the actual limit state functions and the corresponding computational time under the sufficient accuracy. Results of three case studies covering explicit and implicit limit state functions demonstrate the accuracy and efficiency of the proposed method. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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An innovative adaptive Kriging-based parallel system reliability method under error stopping criterion for efficiently analyzing the global reliability sensitivity index |
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https://dx.doi.org/10.1007/s00158-024-03752-8 |
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Zhang, Shutong Li, Fengyuan Chen, Xiangming Wang, Zhe Feng, Kaixuan |
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Zhang, Shutong Li, Fengyuan Chen, Xiangming Wang, Zhe Feng, Kaixuan |
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271602503 |
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10.1007/s00158-024-03752-8 |
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2024-07-03T13:32:42.836Z |
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| score |
7.3999033 |