An Adaptive Line Sampling Method for Reliability Analysis

Abstract Rare event probability estimation imposes serious difficulties to the conventional simulation methods like crude Monte Carlo. Several methods have been put forward to address this issue. Line sampling, directional simulation, importance sampling and subset simulation are more known among ot...
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Autor*in:	Shayanfar, Mohsen Ali [verfasserIn] Barkhordari, Mohammad Ali Barkhori, Moien Rakhshanimehr, Mehrollah

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Reliability analysis Line sampling Design point Monte Carlo simulation

Anmerkung:	© Shiraz University 2017

Übergeordnetes Werk:	Enthalten in: Iranian journal of science and technology - Shiraz : Shiraz University, 2001, 41(2017), 3 vom: 25. Juli, Seite 275-282
Übergeordnetes Werk:	volume:41 ; year:2017 ; number:3 ; day:25 ; month:07 ; pages:275-282

Links:	Volltext

DOI / URN:	10.1007/s40996-017-0070-3

Katalog-ID:	SPR038044439

Internformat


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245	1	3	\|a An Adaptive Line Sampling Method for Reliability Analysis
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520			\|a Abstract Rare event probability estimation imposes serious difficulties to the conventional simulation methods like crude Monte Carlo. Several methods have been put forward to address this issue. Line sampling, directional simulation, importance sampling and subset simulation are more known among other methods. This paper aims at improving the line sampling method. The proposed method iteratively adapts the line sampling procedure to the limit state surface and at the end yields an estimation of the failure probability with the required coefficient of variation. The proposed method is compared to the conventional line sampling via well-known academic problems. The results show considerable improvement over the conventional line sampling method.
650		4	\|a Reliability analysis \|7 (dpeaa)DE-He213
650		4	\|a Line sampling \|7 (dpeaa)DE-He213
650		4	\|a Design point \|7 (dpeaa)DE-He213
650		4	\|a Monte Carlo simulation \|7 (dpeaa)DE-He213
700	1		\|a Barkhordari, Mohammad Ali \|4 aut
700	1		\|a Barkhori, Moien \|4 aut
700	1		\|a Rakhshanimehr, Mehrollah \|4 aut
773	0	8	\|i Enthalten in \|t Iranian journal of science and technology \|d Shiraz : Shiraz University, 2001 \|g 41(2017), 3 vom: 25. Juli, Seite 275-282 \|w (DE-627)844238023 \|w (DE-600)2843076-1 \|x 2364-1843 \|7 nnns
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912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_165
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
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912			\|a GBV_ILN_2107
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912			\|a GBV_ILN_2470
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912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
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allfields	10.1007/s40996-017-0070-3 doi (DE-627)SPR038044439 (SPR)s40996-017-0070-3-e DE-627 ger DE-627 rakwb eng Shayanfar, Mohsen Ali verfasserin aut An Adaptive Line Sampling Method for Reliability Analysis 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2017 Abstract Rare event probability estimation imposes serious difficulties to the conventional simulation methods like crude Monte Carlo. Several methods have been put forward to address this issue. Line sampling, directional simulation, importance sampling and subset simulation are more known among other methods. This paper aims at improving the line sampling method. The proposed method iteratively adapts the line sampling procedure to the limit state surface and at the end yields an estimation of the failure probability with the required coefficient of variation. The proposed method is compared to the conventional line sampling via well-known academic problems. The results show considerable improvement over the conventional line sampling method. Reliability analysis (dpeaa)DE-He213 Line sampling (dpeaa)DE-He213 Design point (dpeaa)DE-He213 Monte Carlo simulation (dpeaa)DE-He213 Barkhordari, Mohammad Ali aut Barkhori, Moien aut Rakhshanimehr, Mehrollah aut Enthalten in Iranian journal of science and technology Shiraz : Shiraz University, 2001 41(2017), 3 vom: 25. Juli, Seite 275-282 (DE-627)844238023 (DE-600)2843076-1 2364-1843 nnns volume:41 year:2017 number:3 day:25 month:07 pages:275-282 https://dx.doi.org/10.1007/s40996-017-0070-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_2008 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2017 3 25 07 275-282
spelling	10.1007/s40996-017-0070-3 doi (DE-627)SPR038044439 (SPR)s40996-017-0070-3-e DE-627 ger DE-627 rakwb eng Shayanfar, Mohsen Ali verfasserin aut An Adaptive Line Sampling Method for Reliability Analysis 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2017 Abstract Rare event probability estimation imposes serious difficulties to the conventional simulation methods like crude Monte Carlo. Several methods have been put forward to address this issue. Line sampling, directional simulation, importance sampling and subset simulation are more known among other methods. This paper aims at improving the line sampling method. The proposed method iteratively adapts the line sampling procedure to the limit state surface and at the end yields an estimation of the failure probability with the required coefficient of variation. The proposed method is compared to the conventional line sampling via well-known academic problems. The results show considerable improvement over the conventional line sampling method. Reliability analysis (dpeaa)DE-He213 Line sampling (dpeaa)DE-He213 Design point (dpeaa)DE-He213 Monte Carlo simulation (dpeaa)DE-He213 Barkhordari, Mohammad Ali aut Barkhori, Moien aut Rakhshanimehr, Mehrollah aut Enthalten in Iranian journal of science and technology Shiraz : Shiraz University, 2001 41(2017), 3 vom: 25. Juli, Seite 275-282 (DE-627)844238023 (DE-600)2843076-1 2364-1843 nnns volume:41 year:2017 number:3 day:25 month:07 pages:275-282 https://dx.doi.org/10.1007/s40996-017-0070-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_2008 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2017 3 25 07 275-282
allfields_unstemmed	10.1007/s40996-017-0070-3 doi (DE-627)SPR038044439 (SPR)s40996-017-0070-3-e DE-627 ger DE-627 rakwb eng Shayanfar, Mohsen Ali verfasserin aut An Adaptive Line Sampling Method for Reliability Analysis 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2017 Abstract Rare event probability estimation imposes serious difficulties to the conventional simulation methods like crude Monte Carlo. Several methods have been put forward to address this issue. Line sampling, directional simulation, importance sampling and subset simulation are more known among other methods. This paper aims at improving the line sampling method. The proposed method iteratively adapts the line sampling procedure to the limit state surface and at the end yields an estimation of the failure probability with the required coefficient of variation. The proposed method is compared to the conventional line sampling via well-known academic problems. The results show considerable improvement over the conventional line sampling method. Reliability analysis (dpeaa)DE-He213 Line sampling (dpeaa)DE-He213 Design point (dpeaa)DE-He213 Monte Carlo simulation (dpeaa)DE-He213 Barkhordari, Mohammad Ali aut Barkhori, Moien aut Rakhshanimehr, Mehrollah aut Enthalten in Iranian journal of science and technology Shiraz : Shiraz University, 2001 41(2017), 3 vom: 25. Juli, Seite 275-282 (DE-627)844238023 (DE-600)2843076-1 2364-1843 nnns volume:41 year:2017 number:3 day:25 month:07 pages:275-282 https://dx.doi.org/10.1007/s40996-017-0070-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_2008 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2017 3 25 07 275-282
allfieldsGer	10.1007/s40996-017-0070-3 doi (DE-627)SPR038044439 (SPR)s40996-017-0070-3-e DE-627 ger DE-627 rakwb eng Shayanfar, Mohsen Ali verfasserin aut An Adaptive Line Sampling Method for Reliability Analysis 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2017 Abstract Rare event probability estimation imposes serious difficulties to the conventional simulation methods like crude Monte Carlo. Several methods have been put forward to address this issue. Line sampling, directional simulation, importance sampling and subset simulation are more known among other methods. This paper aims at improving the line sampling method. The proposed method iteratively adapts the line sampling procedure to the limit state surface and at the end yields an estimation of the failure probability with the required coefficient of variation. The proposed method is compared to the conventional line sampling via well-known academic problems. The results show considerable improvement over the conventional line sampling method. Reliability analysis (dpeaa)DE-He213 Line sampling (dpeaa)DE-He213 Design point (dpeaa)DE-He213 Monte Carlo simulation (dpeaa)DE-He213 Barkhordari, Mohammad Ali aut Barkhori, Moien aut Rakhshanimehr, Mehrollah aut Enthalten in Iranian journal of science and technology Shiraz : Shiraz University, 2001 41(2017), 3 vom: 25. Juli, Seite 275-282 (DE-627)844238023 (DE-600)2843076-1 2364-1843 nnns volume:41 year:2017 number:3 day:25 month:07 pages:275-282 https://dx.doi.org/10.1007/s40996-017-0070-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_2008 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2017 3 25 07 275-282
allfieldsSound	10.1007/s40996-017-0070-3 doi (DE-627)SPR038044439 (SPR)s40996-017-0070-3-e DE-627 ger DE-627 rakwb eng Shayanfar, Mohsen Ali verfasserin aut An Adaptive Line Sampling Method for Reliability Analysis 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Shiraz University 2017 Abstract Rare event probability estimation imposes serious difficulties to the conventional simulation methods like crude Monte Carlo. Several methods have been put forward to address this issue. Line sampling, directional simulation, importance sampling and subset simulation are more known among other methods. This paper aims at improving the line sampling method. The proposed method iteratively adapts the line sampling procedure to the limit state surface and at the end yields an estimation of the failure probability with the required coefficient of variation. The proposed method is compared to the conventional line sampling via well-known academic problems. The results show considerable improvement over the conventional line sampling method. Reliability analysis (dpeaa)DE-He213 Line sampling (dpeaa)DE-He213 Design point (dpeaa)DE-He213 Monte Carlo simulation (dpeaa)DE-He213 Barkhordari, Mohammad Ali aut Barkhori, Moien aut Rakhshanimehr, Mehrollah aut Enthalten in Iranian journal of science and technology Shiraz : Shiraz University, 2001 41(2017), 3 vom: 25. Juli, Seite 275-282 (DE-627)844238023 (DE-600)2843076-1 2364-1843 nnns volume:41 year:2017 number:3 day:25 month:07 pages:275-282 https://dx.doi.org/10.1007/s40996-017-0070-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_2008 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 41 2017 3 25 07 275-282
language	English
source	Enthalten in Iranian journal of science and technology 41(2017), 3 vom: 25. Juli, Seite 275-282 volume:41 year:2017 number:3 day:25 month:07 pages:275-282
sourceStr	Enthalten in Iranian journal of science and technology 41(2017), 3 vom: 25. Juli, Seite 275-282 volume:41 year:2017 number:3 day:25 month:07 pages:275-282
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Reliability analysis Line sampling Design point Monte Carlo simulation
isfreeaccess_bool	false
container_title	Iranian journal of science and technology
authorswithroles_txt_mv	Shayanfar, Mohsen Ali @@aut@@ Barkhordari, Mohammad Ali @@aut@@ Barkhori, Moien @@aut@@ Rakhshanimehr, Mehrollah @@aut@@
publishDateDaySort_date	2017-07-25T00:00:00Z
hierarchy_top_id	844238023
id	SPR038044439
language_de	englisch
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author	Shayanfar, Mohsen Ali
spellingShingle	Shayanfar, Mohsen Ali misc Reliability analysis misc Line sampling misc Design point misc Monte Carlo simulation An Adaptive Line Sampling Method for Reliability Analysis
authorStr	Shayanfar, Mohsen Ali
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topic_title	An Adaptive Line Sampling Method for Reliability Analysis Reliability analysis (dpeaa)DE-He213 Line sampling (dpeaa)DE-He213 Design point (dpeaa)DE-He213 Monte Carlo simulation (dpeaa)DE-He213
topic	misc Reliability analysis misc Line sampling misc Design point misc Monte Carlo simulation
topic_unstemmed	misc Reliability analysis misc Line sampling misc Design point misc Monte Carlo simulation
topic_browse	misc Reliability analysis misc Line sampling misc Design point misc Monte Carlo simulation
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title	An Adaptive Line Sampling Method for Reliability Analysis
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title_full	An Adaptive Line Sampling Method for Reliability Analysis
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author_browse	Shayanfar, Mohsen Ali Barkhordari, Mohammad Ali Barkhori, Moien Rakhshanimehr, Mehrollah
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format_se	Elektronische Aufsätze
author-letter	Shayanfar, Mohsen Ali
doi_str_mv	10.1007/s40996-017-0070-3
title_sort	adaptive line sampling method for reliability analysis
title_auth	An Adaptive Line Sampling Method for Reliability Analysis
abstract	Abstract Rare event probability estimation imposes serious difficulties to the conventional simulation methods like crude Monte Carlo. Several methods have been put forward to address this issue. Line sampling, directional simulation, importance sampling and subset simulation are more known among other methods. This paper aims at improving the line sampling method. The proposed method iteratively adapts the line sampling procedure to the limit state surface and at the end yields an estimation of the failure probability with the required coefficient of variation. The proposed method is compared to the conventional line sampling via well-known academic problems. The results show considerable improvement over the conventional line sampling method. © Shiraz University 2017
abstractGer	Abstract Rare event probability estimation imposes serious difficulties to the conventional simulation methods like crude Monte Carlo. Several methods have been put forward to address this issue. Line sampling, directional simulation, importance sampling and subset simulation are more known among other methods. This paper aims at improving the line sampling method. The proposed method iteratively adapts the line sampling procedure to the limit state surface and at the end yields an estimation of the failure probability with the required coefficient of variation. The proposed method is compared to the conventional line sampling via well-known academic problems. The results show considerable improvement over the conventional line sampling method. © Shiraz University 2017
abstract_unstemmed	Abstract Rare event probability estimation imposes serious difficulties to the conventional simulation methods like crude Monte Carlo. Several methods have been put forward to address this issue. Line sampling, directional simulation, importance sampling and subset simulation are more known among other methods. This paper aims at improving the line sampling method. The proposed method iteratively adapts the line sampling procedure to the limit state surface and at the end yields an estimation of the failure probability with the required coefficient of variation. The proposed method is compared to the conventional line sampling via well-known academic problems. The results show considerable improvement over the conventional line sampling method. © Shiraz University 2017
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title_short	An Adaptive Line Sampling Method for Reliability Analysis
url	https://dx.doi.org/10.1007/s40996-017-0070-3
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author2	Barkhordari, Mohammad Ali Barkhori, Moien Rakhshanimehr, Mehrollah
author2Str	Barkhordari, Mohammad Ali Barkhori, Moien Rakhshanimehr, Mehrollah
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doi_str	10.1007/s40996-017-0070-3
up_date	2024-07-03T15:53:36.358Z
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Several methods have been put forward to address this issue. Line sampling, directional simulation, importance sampling and subset simulation are more known among other methods. This paper aims at improving the line sampling method. The proposed method iteratively adapts the line sampling procedure to the limit state surface and at the end yields an estimation of the failure probability with the required coefficient of variation. The proposed method is compared to the conventional line sampling via well-known academic problems. 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An Adaptive Line Sampling Method for Reliability Analysis

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