Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability

Time-dependent reliability-based design optimization is an effective tool to guarantee a high reliability of the product during the full life cycle. However, the necessarily repeated probabilistic constraint evaluations bring big computational burden when this tool is applied to the complex engineer...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jiang, Chen [verfasserIn] Yan, Yifang [verfasserIn] Wang, Dapeng [verfasserIn] Qiu, Haobo [verfasserIn] Gao, Liang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Time-dependent reliability-based design optimization Adaptive Kriging modeling Wrong classification probability False classification rate Estimation error of failure probability

Übergeordnetes Werk:	Enthalten in: Reliability engineering & system safety - London [u.a.] : Elsevier Science, 1988, 208
Übergeordnetes Werk:	volume:208

DOI / URN:	10.1016/j.ress.2021.107431

Katalog-ID:	ELV005438519

Internformat


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245	1	0	\|a Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability
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520			\|a Time-dependent reliability-based design optimization is an effective tool to guarantee a high reliability of the product during the full life cycle. However, the necessarily repeated probabilistic constraint evaluations bring big computational burden when this tool is applied to the complex engineering systems. To reduce the computational cost, this work employs Kriging model to approximate the limit states of time-consuming probabilistic constraints, and proposes the global and local Kriging modeling methods respectively based on the wrong-classification probability. The global one aims to reduce the wrong-classification probability in the vicinity of the whole limit states, while the local one focuses on the limits states that are potentially visited by the optimum. Based on the wrong-classification probability, two indices, i.e. false classification rate and estimation error of failure probability, are derived to measure the global and local accuracies of limit states respectively. For the global or local Kriging modeling, the approximated Kriging constraint maximizing the false classification rate or the estimation error of failure probability will be updated by the point with the maximum wrong-classification probability. Results of four case studies demonstrate the efficacy of the proposed methods.
650		4	\|a Time-dependent reliability-based design optimization
650		4	\|a Adaptive Kriging modeling
650		4	\|a Wrong classification probability
650		4	\|a False classification rate
650		4	\|a Estimation error of failure probability
700	1		\|a Yan, Yifang \|e verfasserin \|4 aut
700	1		\|a Wang, Dapeng \|e verfasserin \|4 aut
700	1		\|a Qiu, Haobo \|e verfasserin \|4 aut
700	1		\|a Gao, Liang \|e verfasserin \|4 aut
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allfields	10.1016/j.ress.2021.107431 doi (DE-627)ELV005438519 (ELSEVIER)S0951-8320(21)00003-X DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Jiang, Chen verfasserin aut Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Time-dependent reliability-based design optimization is an effective tool to guarantee a high reliability of the product during the full life cycle. However, the necessarily repeated probabilistic constraint evaluations bring big computational burden when this tool is applied to the complex engineering systems. To reduce the computational cost, this work employs Kriging model to approximate the limit states of time-consuming probabilistic constraints, and proposes the global and local Kriging modeling methods respectively based on the wrong-classification probability. The global one aims to reduce the wrong-classification probability in the vicinity of the whole limit states, while the local one focuses on the limits states that are potentially visited by the optimum. Based on the wrong-classification probability, two indices, i.e. false classification rate and estimation error of failure probability, are derived to measure the global and local accuracies of limit states respectively. For the global or local Kriging modeling, the approximated Kriging constraint maximizing the false classification rate or the estimation error of failure probability will be updated by the point with the maximum wrong-classification probability. Results of four case studies demonstrate the efficacy of the proposed methods. Time-dependent reliability-based design optimization Adaptive Kriging modeling Wrong classification probability False classification rate Estimation error of failure probability Yan, Yifang verfasserin aut Wang, Dapeng verfasserin aut Qiu, Haobo verfasserin aut Gao, Liang verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 208 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:208 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.16 Technische Zuverlässigkeit Instandhaltung 85.38 Qualitätsmanagement AR 208
spelling	10.1016/j.ress.2021.107431 doi (DE-627)ELV005438519 (ELSEVIER)S0951-8320(21)00003-X DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Jiang, Chen verfasserin aut Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Time-dependent reliability-based design optimization is an effective tool to guarantee a high reliability of the product during the full life cycle. However, the necessarily repeated probabilistic constraint evaluations bring big computational burden when this tool is applied to the complex engineering systems. To reduce the computational cost, this work employs Kriging model to approximate the limit states of time-consuming probabilistic constraints, and proposes the global and local Kriging modeling methods respectively based on the wrong-classification probability. The global one aims to reduce the wrong-classification probability in the vicinity of the whole limit states, while the local one focuses on the limits states that are potentially visited by the optimum. Based on the wrong-classification probability, two indices, i.e. false classification rate and estimation error of failure probability, are derived to measure the global and local accuracies of limit states respectively. For the global or local Kriging modeling, the approximated Kriging constraint maximizing the false classification rate or the estimation error of failure probability will be updated by the point with the maximum wrong-classification probability. Results of four case studies demonstrate the efficacy of the proposed methods. Time-dependent reliability-based design optimization Adaptive Kriging modeling Wrong classification probability False classification rate Estimation error of failure probability Yan, Yifang verfasserin aut Wang, Dapeng verfasserin aut Qiu, Haobo verfasserin aut Gao, Liang verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 208 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:208 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.16 Technische Zuverlässigkeit Instandhaltung 85.38 Qualitätsmanagement AR 208
allfields_unstemmed	10.1016/j.ress.2021.107431 doi (DE-627)ELV005438519 (ELSEVIER)S0951-8320(21)00003-X DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Jiang, Chen verfasserin aut Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Time-dependent reliability-based design optimization is an effective tool to guarantee a high reliability of the product during the full life cycle. However, the necessarily repeated probabilistic constraint evaluations bring big computational burden when this tool is applied to the complex engineering systems. To reduce the computational cost, this work employs Kriging model to approximate the limit states of time-consuming probabilistic constraints, and proposes the global and local Kriging modeling methods respectively based on the wrong-classification probability. The global one aims to reduce the wrong-classification probability in the vicinity of the whole limit states, while the local one focuses on the limits states that are potentially visited by the optimum. Based on the wrong-classification probability, two indices, i.e. false classification rate and estimation error of failure probability, are derived to measure the global and local accuracies of limit states respectively. For the global or local Kriging modeling, the approximated Kriging constraint maximizing the false classification rate or the estimation error of failure probability will be updated by the point with the maximum wrong-classification probability. Results of four case studies demonstrate the efficacy of the proposed methods. Time-dependent reliability-based design optimization Adaptive Kriging modeling Wrong classification probability False classification rate Estimation error of failure probability Yan, Yifang verfasserin aut Wang, Dapeng verfasserin aut Qiu, Haobo verfasserin aut Gao, Liang verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 208 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:208 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.16 Technische Zuverlässigkeit Instandhaltung 85.38 Qualitätsmanagement AR 208
allfieldsGer	10.1016/j.ress.2021.107431 doi (DE-627)ELV005438519 (ELSEVIER)S0951-8320(21)00003-X DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Jiang, Chen verfasserin aut Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Time-dependent reliability-based design optimization is an effective tool to guarantee a high reliability of the product during the full life cycle. However, the necessarily repeated probabilistic constraint evaluations bring big computational burden when this tool is applied to the complex engineering systems. To reduce the computational cost, this work employs Kriging model to approximate the limit states of time-consuming probabilistic constraints, and proposes the global and local Kriging modeling methods respectively based on the wrong-classification probability. The global one aims to reduce the wrong-classification probability in the vicinity of the whole limit states, while the local one focuses on the limits states that are potentially visited by the optimum. Based on the wrong-classification probability, two indices, i.e. false classification rate and estimation error of failure probability, are derived to measure the global and local accuracies of limit states respectively. For the global or local Kriging modeling, the approximated Kriging constraint maximizing the false classification rate or the estimation error of failure probability will be updated by the point with the maximum wrong-classification probability. Results of four case studies demonstrate the efficacy of the proposed methods. Time-dependent reliability-based design optimization Adaptive Kriging modeling Wrong classification probability False classification rate Estimation error of failure probability Yan, Yifang verfasserin aut Wang, Dapeng verfasserin aut Qiu, Haobo verfasserin aut Gao, Liang verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 208 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:208 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.16 Technische Zuverlässigkeit Instandhaltung 85.38 Qualitätsmanagement AR 208
allfieldsSound	10.1016/j.ress.2021.107431 doi (DE-627)ELV005438519 (ELSEVIER)S0951-8320(21)00003-X DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Jiang, Chen verfasserin aut Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Time-dependent reliability-based design optimization is an effective tool to guarantee a high reliability of the product during the full life cycle. However, the necessarily repeated probabilistic constraint evaluations bring big computational burden when this tool is applied to the complex engineering systems. To reduce the computational cost, this work employs Kriging model to approximate the limit states of time-consuming probabilistic constraints, and proposes the global and local Kriging modeling methods respectively based on the wrong-classification probability. The global one aims to reduce the wrong-classification probability in the vicinity of the whole limit states, while the local one focuses on the limits states that are potentially visited by the optimum. Based on the wrong-classification probability, two indices, i.e. false classification rate and estimation error of failure probability, are derived to measure the global and local accuracies of limit states respectively. For the global or local Kriging modeling, the approximated Kriging constraint maximizing the false classification rate or the estimation error of failure probability will be updated by the point with the maximum wrong-classification probability. Results of four case studies demonstrate the efficacy of the proposed methods. Time-dependent reliability-based design optimization Adaptive Kriging modeling Wrong classification probability False classification rate Estimation error of failure probability Yan, Yifang verfasserin aut Wang, Dapeng verfasserin aut Qiu, Haobo verfasserin aut Gao, Liang verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 208 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:208 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 50.16 Technische Zuverlässigkeit Instandhaltung 85.38 Qualitätsmanagement AR 208
language	English
source	Enthalten in Reliability engineering & system safety 208 volume:208
sourceStr	Enthalten in Reliability engineering & system safety 208 volume:208
format_phy_str_mv	Article
bklname	Technische Zuverlässigkeit Instandhaltung Qualitätsmanagement
institution	findex.gbv.de
topic_facet	Time-dependent reliability-based design optimization Adaptive Kriging modeling Wrong classification probability False classification rate Estimation error of failure probability
dewey-raw	600
isfreeaccess_bool	false
container_title	Reliability engineering & system safety
authorswithroles_txt_mv	Jiang, Chen @@aut@@ Yan, Yifang @@aut@@ Wang, Dapeng @@aut@@ Qiu, Haobo @@aut@@ Gao, Liang @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	320608743
dewey-sort	3600
id	ELV005438519
language_de	englisch
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author	Jiang, Chen
spellingShingle	Jiang, Chen ddc 600 bkl 50.16 bkl 85.38 misc Time-dependent reliability-based design optimization misc Adaptive Kriging modeling misc Wrong classification probability misc False classification rate misc Estimation error of failure probability Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability
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topic_title	600 DE-600 50.16 bkl 85.38 bkl Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability Time-dependent reliability-based design optimization Adaptive Kriging modeling Wrong classification probability False classification rate Estimation error of failure probability
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title	Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability
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title_full	Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability
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title_sort	global and local kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability
title_auth	Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability
abstract	Time-dependent reliability-based design optimization is an effective tool to guarantee a high reliability of the product during the full life cycle. However, the necessarily repeated probabilistic constraint evaluations bring big computational burden when this tool is applied to the complex engineering systems. To reduce the computational cost, this work employs Kriging model to approximate the limit states of time-consuming probabilistic constraints, and proposes the global and local Kriging modeling methods respectively based on the wrong-classification probability. The global one aims to reduce the wrong-classification probability in the vicinity of the whole limit states, while the local one focuses on the limits states that are potentially visited by the optimum. Based on the wrong-classification probability, two indices, i.e. false classification rate and estimation error of failure probability, are derived to measure the global and local accuracies of limit states respectively. For the global or local Kriging modeling, the approximated Kriging constraint maximizing the false classification rate or the estimation error of failure probability will be updated by the point with the maximum wrong-classification probability. Results of four case studies demonstrate the efficacy of the proposed methods.
abstractGer	Time-dependent reliability-based design optimization is an effective tool to guarantee a high reliability of the product during the full life cycle. However, the necessarily repeated probabilistic constraint evaluations bring big computational burden when this tool is applied to the complex engineering systems. To reduce the computational cost, this work employs Kriging model to approximate the limit states of time-consuming probabilistic constraints, and proposes the global and local Kriging modeling methods respectively based on the wrong-classification probability. The global one aims to reduce the wrong-classification probability in the vicinity of the whole limit states, while the local one focuses on the limits states that are potentially visited by the optimum. Based on the wrong-classification probability, two indices, i.e. false classification rate and estimation error of failure probability, are derived to measure the global and local accuracies of limit states respectively. For the global or local Kriging modeling, the approximated Kriging constraint maximizing the false classification rate or the estimation error of failure probability will be updated by the point with the maximum wrong-classification probability. Results of four case studies demonstrate the efficacy of the proposed methods.
abstract_unstemmed	Time-dependent reliability-based design optimization is an effective tool to guarantee a high reliability of the product during the full life cycle. However, the necessarily repeated probabilistic constraint evaluations bring big computational burden when this tool is applied to the complex engineering systems. To reduce the computational cost, this work employs Kriging model to approximate the limit states of time-consuming probabilistic constraints, and proposes the global and local Kriging modeling methods respectively based on the wrong-classification probability. The global one aims to reduce the wrong-classification probability in the vicinity of the whole limit states, while the local one focuses on the limits states that are potentially visited by the optimum. Based on the wrong-classification probability, two indices, i.e. false classification rate and estimation error of failure probability, are derived to measure the global and local accuracies of limit states respectively. For the global or local Kriging modeling, the approximated Kriging constraint maximizing the false classification rate or the estimation error of failure probability will be updated by the point with the maximum wrong-classification probability. Results of four case studies demonstrate the efficacy of the proposed methods.
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title_short	Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability
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author2	Yan, Yifang Wang, Dapeng Qiu, Haobo Gao, Liang
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Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability

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