Hybrid MCS-FORM approach to solve inverse fracture mechanics reliability problems
Abstract Classical approaches to inverse reliability analysis, and to reliability-based design optimization, require evaluation of limit state gradients. This can be an issue for highly non-linear time-variant reliability problems. Stochastic fracture mechanics, for instance, involves non-linear cra...
Ausführliche Beschreibung
Autor*in: |
Gomes, Wellison José de Santana [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Übergeordnetes Werk: |
Enthalten in: Structural and multidisciplinary optimization - Berlin : Springer, 1989, 65(2022), 3 vom: 08. Feb. |
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Übergeordnetes Werk: |
volume:65 ; year:2022 ; number:3 ; day:08 ; month:02 |
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DOI / URN: |
10.1007/s00158-022-03182-4 |
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Katalog-ID: |
SPR046179836 |
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520 | |a Abstract Classical approaches to inverse reliability analysis, and to reliability-based design optimization, require evaluation of limit state gradients. This can be an issue for highly non-linear time-variant reliability problems. Stochastic fracture mechanics, for instance, involves non-linear crack propagation and millions of load cycles; also hundreds to millions of random variables. Gradient computation becomes highly unstable at the end of fatigue life, as does the crack growth process. In this paper, a hybrid approach is proposed for solving this type of problem: it explores and combines the best of Monte Carlo Simulation (MCS), First-Order Reliability Method (FORM) and a root-finding method. The non-linear crack propagation phase of the problem is handled by MCS; the final fracture problem is handled by FORM; and the allowable crack size, required to impose a minimum lifetime reliability, is obtained by simple root-finding, among the set of initial crack size samples. Efficiency is achieved: (a) by classifying initial crack size samples, and computing only those terms effectively contributing non-zero probabilities to the integral; and (b) by solving the optimization problem using the information acquired from a single Monte Carlo run. The proposed hybrid approach is employed in the solution of typical crack propagation problems: it is shown that it gains efficiency when the target reliability and the number of load cycles are large, as expected in practical problems of this kind. It is also shown that it converges to the reference solutions, being them exact, when available, or usual MC-based solutions, while taking only a fraction of the computational time required by the latter. | ||
650 | 4 | |a Structural reliability |7 (dpeaa)DE-He213 | |
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650 | 4 | |a Pipeline safety management |7 (dpeaa)DE-He213 | |
650 | 4 | |a Reliability-based design optimization |7 (dpeaa)DE-He213 | |
700 | 1 | |a Garmbis, Alexandre Galiani |4 aut | |
700 | 1 | |a Beck, André Teófilo |0 (orcid)0000-0003-4127-5337 |4 aut | |
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10.1007/s00158-022-03182-4 doi (DE-627)SPR046179836 (SPR)s00158-022-03182-4-e DE-627 ger DE-627 rakwb eng Gomes, Wellison José de Santana verfasserin aut Hybrid MCS-FORM approach to solve inverse fracture mechanics reliability problems 2022 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 2022 Abstract Classical approaches to inverse reliability analysis, and to reliability-based design optimization, require evaluation of limit state gradients. This can be an issue for highly non-linear time-variant reliability problems. Stochastic fracture mechanics, for instance, involves non-linear crack propagation and millions of load cycles; also hundreds to millions of random variables. Gradient computation becomes highly unstable at the end of fatigue life, as does the crack growth process. In this paper, a hybrid approach is proposed for solving this type of problem: it explores and combines the best of Monte Carlo Simulation (MCS), First-Order Reliability Method (FORM) and a root-finding method. The non-linear crack propagation phase of the problem is handled by MCS; the final fracture problem is handled by FORM; and the allowable crack size, required to impose a minimum lifetime reliability, is obtained by simple root-finding, among the set of initial crack size samples. Efficiency is achieved: (a) by classifying initial crack size samples, and computing only those terms effectively contributing non-zero probabilities to the integral; and (b) by solving the optimization problem using the information acquired from a single Monte Carlo run. The proposed hybrid approach is employed in the solution of typical crack propagation problems: it is shown that it gains efficiency when the target reliability and the number of load cycles are large, as expected in practical problems of this kind. It is also shown that it converges to the reference solutions, being them exact, when available, or usual MC-based solutions, while taking only a fraction of the computational time required by the latter. Structural reliability (dpeaa)DE-He213 Inverse reliability (dpeaa)DE-He213 Stochastic fracture mechanics (dpeaa)DE-He213 Pipeline safety management (dpeaa)DE-He213 Reliability-based design optimization (dpeaa)DE-He213 Garmbis, Alexandre Galiani aut Beck, André Teófilo (orcid)0000-0003-4127-5337 aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 65(2022), 3 vom: 08. Feb. (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:65 year:2022 number:3 day:08 month:02 https://dx.doi.org/10.1007/s00158-022-03182-4 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_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_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_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 AR 65 2022 3 08 02 |
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10.1007/s00158-022-03182-4 doi (DE-627)SPR046179836 (SPR)s00158-022-03182-4-e DE-627 ger DE-627 rakwb eng Gomes, Wellison José de Santana verfasserin aut Hybrid MCS-FORM approach to solve inverse fracture mechanics reliability problems 2022 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 2022 Abstract Classical approaches to inverse reliability analysis, and to reliability-based design optimization, require evaluation of limit state gradients. This can be an issue for highly non-linear time-variant reliability problems. Stochastic fracture mechanics, for instance, involves non-linear crack propagation and millions of load cycles; also hundreds to millions of random variables. Gradient computation becomes highly unstable at the end of fatigue life, as does the crack growth process. In this paper, a hybrid approach is proposed for solving this type of problem: it explores and combines the best of Monte Carlo Simulation (MCS), First-Order Reliability Method (FORM) and a root-finding method. The non-linear crack propagation phase of the problem is handled by MCS; the final fracture problem is handled by FORM; and the allowable crack size, required to impose a minimum lifetime reliability, is obtained by simple root-finding, among the set of initial crack size samples. Efficiency is achieved: (a) by classifying initial crack size samples, and computing only those terms effectively contributing non-zero probabilities to the integral; and (b) by solving the optimization problem using the information acquired from a single Monte Carlo run. The proposed hybrid approach is employed in the solution of typical crack propagation problems: it is shown that it gains efficiency when the target reliability and the number of load cycles are large, as expected in practical problems of this kind. It is also shown that it converges to the reference solutions, being them exact, when available, or usual MC-based solutions, while taking only a fraction of the computational time required by the latter. Structural reliability (dpeaa)DE-He213 Inverse reliability (dpeaa)DE-He213 Stochastic fracture mechanics (dpeaa)DE-He213 Pipeline safety management (dpeaa)DE-He213 Reliability-based design optimization (dpeaa)DE-He213 Garmbis, Alexandre Galiani aut Beck, André Teófilo (orcid)0000-0003-4127-5337 aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 65(2022), 3 vom: 08. Feb. (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:65 year:2022 number:3 day:08 month:02 https://dx.doi.org/10.1007/s00158-022-03182-4 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_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_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_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 AR 65 2022 3 08 02 |
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10.1007/s00158-022-03182-4 doi (DE-627)SPR046179836 (SPR)s00158-022-03182-4-e DE-627 ger DE-627 rakwb eng Gomes, Wellison José de Santana verfasserin aut Hybrid MCS-FORM approach to solve inverse fracture mechanics reliability problems 2022 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 2022 Abstract Classical approaches to inverse reliability analysis, and to reliability-based design optimization, require evaluation of limit state gradients. This can be an issue for highly non-linear time-variant reliability problems. Stochastic fracture mechanics, for instance, involves non-linear crack propagation and millions of load cycles; also hundreds to millions of random variables. Gradient computation becomes highly unstable at the end of fatigue life, as does the crack growth process. In this paper, a hybrid approach is proposed for solving this type of problem: it explores and combines the best of Monte Carlo Simulation (MCS), First-Order Reliability Method (FORM) and a root-finding method. The non-linear crack propagation phase of the problem is handled by MCS; the final fracture problem is handled by FORM; and the allowable crack size, required to impose a minimum lifetime reliability, is obtained by simple root-finding, among the set of initial crack size samples. Efficiency is achieved: (a) by classifying initial crack size samples, and computing only those terms effectively contributing non-zero probabilities to the integral; and (b) by solving the optimization problem using the information acquired from a single Monte Carlo run. The proposed hybrid approach is employed in the solution of typical crack propagation problems: it is shown that it gains efficiency when the target reliability and the number of load cycles are large, as expected in practical problems of this kind. It is also shown that it converges to the reference solutions, being them exact, when available, or usual MC-based solutions, while taking only a fraction of the computational time required by the latter. Structural reliability (dpeaa)DE-He213 Inverse reliability (dpeaa)DE-He213 Stochastic fracture mechanics (dpeaa)DE-He213 Pipeline safety management (dpeaa)DE-He213 Reliability-based design optimization (dpeaa)DE-He213 Garmbis, Alexandre Galiani aut Beck, André Teófilo (orcid)0000-0003-4127-5337 aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 65(2022), 3 vom: 08. Feb. (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:65 year:2022 number:3 day:08 month:02 https://dx.doi.org/10.1007/s00158-022-03182-4 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_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_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_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 AR 65 2022 3 08 02 |
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10.1007/s00158-022-03182-4 doi (DE-627)SPR046179836 (SPR)s00158-022-03182-4-e DE-627 ger DE-627 rakwb eng Gomes, Wellison José de Santana verfasserin aut Hybrid MCS-FORM approach to solve inverse fracture mechanics reliability problems 2022 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 2022 Abstract Classical approaches to inverse reliability analysis, and to reliability-based design optimization, require evaluation of limit state gradients. This can be an issue for highly non-linear time-variant reliability problems. Stochastic fracture mechanics, for instance, involves non-linear crack propagation and millions of load cycles; also hundreds to millions of random variables. Gradient computation becomes highly unstable at the end of fatigue life, as does the crack growth process. In this paper, a hybrid approach is proposed for solving this type of problem: it explores and combines the best of Monte Carlo Simulation (MCS), First-Order Reliability Method (FORM) and a root-finding method. The non-linear crack propagation phase of the problem is handled by MCS; the final fracture problem is handled by FORM; and the allowable crack size, required to impose a minimum lifetime reliability, is obtained by simple root-finding, among the set of initial crack size samples. Efficiency is achieved: (a) by classifying initial crack size samples, and computing only those terms effectively contributing non-zero probabilities to the integral; and (b) by solving the optimization problem using the information acquired from a single Monte Carlo run. The proposed hybrid approach is employed in the solution of typical crack propagation problems: it is shown that it gains efficiency when the target reliability and the number of load cycles are large, as expected in practical problems of this kind. It is also shown that it converges to the reference solutions, being them exact, when available, or usual MC-based solutions, while taking only a fraction of the computational time required by the latter. Structural reliability (dpeaa)DE-He213 Inverse reliability (dpeaa)DE-He213 Stochastic fracture mechanics (dpeaa)DE-He213 Pipeline safety management (dpeaa)DE-He213 Reliability-based design optimization (dpeaa)DE-He213 Garmbis, Alexandre Galiani aut Beck, André Teófilo (orcid)0000-0003-4127-5337 aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 65(2022), 3 vom: 08. Feb. (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:65 year:2022 number:3 day:08 month:02 https://dx.doi.org/10.1007/s00158-022-03182-4 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_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_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_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 AR 65 2022 3 08 02 |
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10.1007/s00158-022-03182-4 doi (DE-627)SPR046179836 (SPR)s00158-022-03182-4-e DE-627 ger DE-627 rakwb eng Gomes, Wellison José de Santana verfasserin aut Hybrid MCS-FORM approach to solve inverse fracture mechanics reliability problems 2022 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 2022 Abstract Classical approaches to inverse reliability analysis, and to reliability-based design optimization, require evaluation of limit state gradients. This can be an issue for highly non-linear time-variant reliability problems. Stochastic fracture mechanics, for instance, involves non-linear crack propagation and millions of load cycles; also hundreds to millions of random variables. Gradient computation becomes highly unstable at the end of fatigue life, as does the crack growth process. In this paper, a hybrid approach is proposed for solving this type of problem: it explores and combines the best of Monte Carlo Simulation (MCS), First-Order Reliability Method (FORM) and a root-finding method. The non-linear crack propagation phase of the problem is handled by MCS; the final fracture problem is handled by FORM; and the allowable crack size, required to impose a minimum lifetime reliability, is obtained by simple root-finding, among the set of initial crack size samples. Efficiency is achieved: (a) by classifying initial crack size samples, and computing only those terms effectively contributing non-zero probabilities to the integral; and (b) by solving the optimization problem using the information acquired from a single Monte Carlo run. The proposed hybrid approach is employed in the solution of typical crack propagation problems: it is shown that it gains efficiency when the target reliability and the number of load cycles are large, as expected in practical problems of this kind. It is also shown that it converges to the reference solutions, being them exact, when available, or usual MC-based solutions, while taking only a fraction of the computational time required by the latter. Structural reliability (dpeaa)DE-He213 Inverse reliability (dpeaa)DE-He213 Stochastic fracture mechanics (dpeaa)DE-He213 Pipeline safety management (dpeaa)DE-He213 Reliability-based design optimization (dpeaa)DE-He213 Garmbis, Alexandre Galiani aut Beck, André Teófilo (orcid)0000-0003-4127-5337 aut Enthalten in Structural and multidisciplinary optimization Berlin : Springer, 1989 65(2022), 3 vom: 08. Feb. (DE-627)271602503 (DE-600)1481279-4 1615-1488 nnns volume:65 year:2022 number:3 day:08 month:02 https://dx.doi.org/10.1007/s00158-022-03182-4 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_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_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_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 AR 65 2022 3 08 02 |
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Enthalten in Structural and multidisciplinary optimization 65(2022), 3 vom: 08. Feb. volume:65 year:2022 number:3 day:08 month:02 |
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Gomes, Wellison José de Santana @@aut@@ Garmbis, Alexandre Galiani @@aut@@ Beck, André Teófilo @@aut@@ |
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Gomes, Wellison José de Santana |
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Gomes, Wellison José de Santana misc Structural reliability misc Inverse reliability misc Stochastic fracture mechanics misc Pipeline safety management misc Reliability-based design optimization Hybrid MCS-FORM approach to solve inverse fracture mechanics reliability problems |
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Hybrid MCS-FORM approach to solve inverse fracture mechanics reliability problems Structural reliability (dpeaa)DE-He213 Inverse reliability (dpeaa)DE-He213 Stochastic fracture mechanics (dpeaa)DE-He213 Pipeline safety management (dpeaa)DE-He213 Reliability-based design optimization (dpeaa)DE-He213 |
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hybrid mcs-form approach to solve inverse fracture mechanics reliability problems |
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Hybrid MCS-FORM approach to solve inverse fracture mechanics reliability problems |
abstract |
Abstract Classical approaches to inverse reliability analysis, and to reliability-based design optimization, require evaluation of limit state gradients. This can be an issue for highly non-linear time-variant reliability problems. Stochastic fracture mechanics, for instance, involves non-linear crack propagation and millions of load cycles; also hundreds to millions of random variables. Gradient computation becomes highly unstable at the end of fatigue life, as does the crack growth process. In this paper, a hybrid approach is proposed for solving this type of problem: it explores and combines the best of Monte Carlo Simulation (MCS), First-Order Reliability Method (FORM) and a root-finding method. The non-linear crack propagation phase of the problem is handled by MCS; the final fracture problem is handled by FORM; and the allowable crack size, required to impose a minimum lifetime reliability, is obtained by simple root-finding, among the set of initial crack size samples. Efficiency is achieved: (a) by classifying initial crack size samples, and computing only those terms effectively contributing non-zero probabilities to the integral; and (b) by solving the optimization problem using the information acquired from a single Monte Carlo run. The proposed hybrid approach is employed in the solution of typical crack propagation problems: it is shown that it gains efficiency when the target reliability and the number of load cycles are large, as expected in practical problems of this kind. It is also shown that it converges to the reference solutions, being them exact, when available, or usual MC-based solutions, while taking only a fraction of the computational time required by the latter. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
abstractGer |
Abstract Classical approaches to inverse reliability analysis, and to reliability-based design optimization, require evaluation of limit state gradients. This can be an issue for highly non-linear time-variant reliability problems. Stochastic fracture mechanics, for instance, involves non-linear crack propagation and millions of load cycles; also hundreds to millions of random variables. Gradient computation becomes highly unstable at the end of fatigue life, as does the crack growth process. In this paper, a hybrid approach is proposed for solving this type of problem: it explores and combines the best of Monte Carlo Simulation (MCS), First-Order Reliability Method (FORM) and a root-finding method. The non-linear crack propagation phase of the problem is handled by MCS; the final fracture problem is handled by FORM; and the allowable crack size, required to impose a minimum lifetime reliability, is obtained by simple root-finding, among the set of initial crack size samples. Efficiency is achieved: (a) by classifying initial crack size samples, and computing only those terms effectively contributing non-zero probabilities to the integral; and (b) by solving the optimization problem using the information acquired from a single Monte Carlo run. The proposed hybrid approach is employed in the solution of typical crack propagation problems: it is shown that it gains efficiency when the target reliability and the number of load cycles are large, as expected in practical problems of this kind. It is also shown that it converges to the reference solutions, being them exact, when available, or usual MC-based solutions, while taking only a fraction of the computational time required by the latter. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
abstract_unstemmed |
Abstract Classical approaches to inverse reliability analysis, and to reliability-based design optimization, require evaluation of limit state gradients. This can be an issue for highly non-linear time-variant reliability problems. Stochastic fracture mechanics, for instance, involves non-linear crack propagation and millions of load cycles; also hundreds to millions of random variables. Gradient computation becomes highly unstable at the end of fatigue life, as does the crack growth process. In this paper, a hybrid approach is proposed for solving this type of problem: it explores and combines the best of Monte Carlo Simulation (MCS), First-Order Reliability Method (FORM) and a root-finding method. The non-linear crack propagation phase of the problem is handled by MCS; the final fracture problem is handled by FORM; and the allowable crack size, required to impose a minimum lifetime reliability, is obtained by simple root-finding, among the set of initial crack size samples. Efficiency is achieved: (a) by classifying initial crack size samples, and computing only those terms effectively contributing non-zero probabilities to the integral; and (b) by solving the optimization problem using the information acquired from a single Monte Carlo run. The proposed hybrid approach is employed in the solution of typical crack propagation problems: it is shown that it gains efficiency when the target reliability and the number of load cycles are large, as expected in practical problems of this kind. It is also shown that it converges to the reference solutions, being them exact, when available, or usual MC-based solutions, while taking only a fraction of the computational time required by the latter. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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title_short |
Hybrid MCS-FORM approach to solve inverse fracture mechanics reliability problems |
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https://dx.doi.org/10.1007/s00158-022-03182-4 |
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Garmbis, Alexandre Galiani Beck, André Teófilo |
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Garmbis, Alexandre Galiani Beck, André Teófilo |
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10.1007/s00158-022-03182-4 |
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|
score |
7.4017067 |