Stochastic modeling of corrosion growth
Corrosion is a major threat to the structural integrity and safe operation of infrastructures throughout the world. Corrosion growth modeling is important in structure maintenance planning. Existing models focus on the maximum corrosion pit depth growth which may lead to inaccurate life predictions...
Ausführliche Beschreibung
Autor*in: |
Wang, Changxi [verfasserIn] Elsayed, Elsayed A. [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Reliability engineering & system safety - London [u.a.] : Elsevier Science, 1988, 204 |
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Übergeordnetes Werk: |
volume:204 |
DOI / URN: |
10.1016/j.ress.2020.107120 |
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Katalog-ID: |
ELV004906829 |
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520 | |a Corrosion is a major threat to the structural integrity and safe operation of infrastructures throughout the world. Corrosion growth modeling is important in structure maintenance planning. Existing models focus on the maximum corrosion pit depth growth which may lead to inaccurate life predictions since the corrosion volume growth may result in failures before the depth reaches its failure threshold. We develop a stochastic model that characterizes both corrosion volume and depth growth. The distribution of volume growth increments with time and the reliability estimate based on the corrosion depth, the corrosion volume growth and their combined effect are obtained. The influence of stresses, which include the relative humidity, pH level and temperature is incorporated into the model based on the physics of the corrosion reaction mechanisms. Field corrosion data are used for the model's validation. The proposed model results in more accurate predictions of the remaining lives compared with the existing models. | ||
650 | 4 | |a Corrosion volume growth | |
650 | 4 | |a Gamma process | |
650 | 4 | |a Corrosion pit | |
650 | 4 | |a Remaining life prediction | |
650 | 4 | |a Corrosion growth under stresses | |
700 | 1 | |a Elsayed, Elsayed A. |e verfasserin |4 aut | |
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2020 |
allfields |
10.1016/j.ress.2020.107120 doi (DE-627)ELV004906829 (ELSEVIER)S0951-8320(20)30621-9 DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Wang, Changxi verfasserin (orcid)0000-0002-8299-6395 aut Stochastic modeling of corrosion growth 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Corrosion is a major threat to the structural integrity and safe operation of infrastructures throughout the world. Corrosion growth modeling is important in structure maintenance planning. Existing models focus on the maximum corrosion pit depth growth which may lead to inaccurate life predictions since the corrosion volume growth may result in failures before the depth reaches its failure threshold. We develop a stochastic model that characterizes both corrosion volume and depth growth. The distribution of volume growth increments with time and the reliability estimate based on the corrosion depth, the corrosion volume growth and their combined effect are obtained. The influence of stresses, which include the relative humidity, pH level and temperature is incorporated into the model based on the physics of the corrosion reaction mechanisms. Field corrosion data are used for the model's validation. The proposed model results in more accurate predictions of the remaining lives compared with the existing models. Corrosion volume growth Gamma process Corrosion pit Remaining life prediction Corrosion growth under stresses Elsayed, Elsayed A. verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 204 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:204 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 204 |
spelling |
10.1016/j.ress.2020.107120 doi (DE-627)ELV004906829 (ELSEVIER)S0951-8320(20)30621-9 DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Wang, Changxi verfasserin (orcid)0000-0002-8299-6395 aut Stochastic modeling of corrosion growth 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Corrosion is a major threat to the structural integrity and safe operation of infrastructures throughout the world. Corrosion growth modeling is important in structure maintenance planning. Existing models focus on the maximum corrosion pit depth growth which may lead to inaccurate life predictions since the corrosion volume growth may result in failures before the depth reaches its failure threshold. We develop a stochastic model that characterizes both corrosion volume and depth growth. The distribution of volume growth increments with time and the reliability estimate based on the corrosion depth, the corrosion volume growth and their combined effect are obtained. The influence of stresses, which include the relative humidity, pH level and temperature is incorporated into the model based on the physics of the corrosion reaction mechanisms. Field corrosion data are used for the model's validation. The proposed model results in more accurate predictions of the remaining lives compared with the existing models. Corrosion volume growth Gamma process Corrosion pit Remaining life prediction Corrosion growth under stresses Elsayed, Elsayed A. verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 204 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:204 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 204 |
allfields_unstemmed |
10.1016/j.ress.2020.107120 doi (DE-627)ELV004906829 (ELSEVIER)S0951-8320(20)30621-9 DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Wang, Changxi verfasserin (orcid)0000-0002-8299-6395 aut Stochastic modeling of corrosion growth 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Corrosion is a major threat to the structural integrity and safe operation of infrastructures throughout the world. Corrosion growth modeling is important in structure maintenance planning. Existing models focus on the maximum corrosion pit depth growth which may lead to inaccurate life predictions since the corrosion volume growth may result in failures before the depth reaches its failure threshold. We develop a stochastic model that characterizes both corrosion volume and depth growth. The distribution of volume growth increments with time and the reliability estimate based on the corrosion depth, the corrosion volume growth and their combined effect are obtained. The influence of stresses, which include the relative humidity, pH level and temperature is incorporated into the model based on the physics of the corrosion reaction mechanisms. Field corrosion data are used for the model's validation. The proposed model results in more accurate predictions of the remaining lives compared with the existing models. Corrosion volume growth Gamma process Corrosion pit Remaining life prediction Corrosion growth under stresses Elsayed, Elsayed A. verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 204 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:204 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 204 |
allfieldsGer |
10.1016/j.ress.2020.107120 doi (DE-627)ELV004906829 (ELSEVIER)S0951-8320(20)30621-9 DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Wang, Changxi verfasserin (orcid)0000-0002-8299-6395 aut Stochastic modeling of corrosion growth 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Corrosion is a major threat to the structural integrity and safe operation of infrastructures throughout the world. Corrosion growth modeling is important in structure maintenance planning. Existing models focus on the maximum corrosion pit depth growth which may lead to inaccurate life predictions since the corrosion volume growth may result in failures before the depth reaches its failure threshold. We develop a stochastic model that characterizes both corrosion volume and depth growth. The distribution of volume growth increments with time and the reliability estimate based on the corrosion depth, the corrosion volume growth and their combined effect are obtained. The influence of stresses, which include the relative humidity, pH level and temperature is incorporated into the model based on the physics of the corrosion reaction mechanisms. Field corrosion data are used for the model's validation. The proposed model results in more accurate predictions of the remaining lives compared with the existing models. Corrosion volume growth Gamma process Corrosion pit Remaining life prediction Corrosion growth under stresses Elsayed, Elsayed A. verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 204 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:204 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 204 |
allfieldsSound |
10.1016/j.ress.2020.107120 doi (DE-627)ELV004906829 (ELSEVIER)S0951-8320(20)30621-9 DE-627 ger DE-627 rda eng 600 DE-600 50.16 bkl 85.38 bkl Wang, Changxi verfasserin (orcid)0000-0002-8299-6395 aut Stochastic modeling of corrosion growth 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Corrosion is a major threat to the structural integrity and safe operation of infrastructures throughout the world. Corrosion growth modeling is important in structure maintenance planning. Existing models focus on the maximum corrosion pit depth growth which may lead to inaccurate life predictions since the corrosion volume growth may result in failures before the depth reaches its failure threshold. We develop a stochastic model that characterizes both corrosion volume and depth growth. The distribution of volume growth increments with time and the reliability estimate based on the corrosion depth, the corrosion volume growth and their combined effect are obtained. The influence of stresses, which include the relative humidity, pH level and temperature is incorporated into the model based on the physics of the corrosion reaction mechanisms. Field corrosion data are used for the model's validation. The proposed model results in more accurate predictions of the remaining lives compared with the existing models. Corrosion volume growth Gamma process Corrosion pit Remaining life prediction Corrosion growth under stresses Elsayed, Elsayed A. verfasserin aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 204 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:204 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 204 |
language |
English |
source |
Enthalten in Reliability engineering & system safety 204 volume:204 |
sourceStr |
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Wang, Changxi @@aut@@ Elsayed, Elsayed A. @@aut@@ |
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Corrosion is a major threat to the structural integrity and safe operation of infrastructures throughout the world. Corrosion growth modeling is important in structure maintenance planning. Existing models focus on the maximum corrosion pit depth growth which may lead to inaccurate life predictions since the corrosion volume growth may result in failures before the depth reaches its failure threshold. We develop a stochastic model that characterizes both corrosion volume and depth growth. The distribution of volume growth increments with time and the reliability estimate based on the corrosion depth, the corrosion volume growth and their combined effect are obtained. The influence of stresses, which include the relative humidity, pH level and temperature is incorporated into the model based on the physics of the corrosion reaction mechanisms. Field corrosion data are used for the model's validation. The proposed model results in more accurate predictions of the remaining lives compared with the existing models. |
abstractGer |
Corrosion is a major threat to the structural integrity and safe operation of infrastructures throughout the world. Corrosion growth modeling is important in structure maintenance planning. Existing models focus on the maximum corrosion pit depth growth which may lead to inaccurate life predictions since the corrosion volume growth may result in failures before the depth reaches its failure threshold. We develop a stochastic model that characterizes both corrosion volume and depth growth. The distribution of volume growth increments with time and the reliability estimate based on the corrosion depth, the corrosion volume growth and their combined effect are obtained. The influence of stresses, which include the relative humidity, pH level and temperature is incorporated into the model based on the physics of the corrosion reaction mechanisms. Field corrosion data are used for the model's validation. The proposed model results in more accurate predictions of the remaining lives compared with the existing models. |
abstract_unstemmed |
Corrosion is a major threat to the structural integrity and safe operation of infrastructures throughout the world. Corrosion growth modeling is important in structure maintenance planning. Existing models focus on the maximum corrosion pit depth growth which may lead to inaccurate life predictions since the corrosion volume growth may result in failures before the depth reaches its failure threshold. We develop a stochastic model that characterizes both corrosion volume and depth growth. The distribution of volume growth increments with time and the reliability estimate based on the corrosion depth, the corrosion volume growth and their combined effect are obtained. The influence of stresses, which include the relative humidity, pH level and temperature is incorporated into the model based on the physics of the corrosion reaction mechanisms. Field corrosion data are used for the model's validation. The proposed model results in more accurate predictions of the remaining lives compared with the existing models. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV004906829</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205154617.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230503s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ress.2020.107120</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV004906829</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0951-8320(20)30621-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">600</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.16</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">85.38</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Changxi</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-8299-6395</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Stochastic modeling of corrosion growth</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Corrosion is a major threat to the structural integrity and safe operation of infrastructures throughout the world. Corrosion growth modeling is important in structure maintenance planning. Existing models focus on the maximum corrosion pit depth growth which may lead to inaccurate life predictions since the corrosion volume growth may result in failures before the depth reaches its failure threshold. We develop a stochastic model that characterizes both corrosion volume and depth growth. The distribution of volume growth increments with time and the reliability estimate based on the corrosion depth, the corrosion volume growth and their combined effect are obtained. The influence of stresses, which include the relative humidity, pH level and temperature is incorporated into the model based on the physics of the corrosion reaction mechanisms. Field corrosion data are used for the model's validation. 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