An adaptive structural dominant failure modes searching method based on graph neural network
Dominant failure modes (DFMs) of structural systems are integral to life prediction and reliability assessment. However, the computational efficiency of existing DFMs searching methods is constrained by neglecting the structural non-Euclidean properties. To break free from this shackle, this paper p...
Ausführliche Beschreibung
Autor*in: |
Tian, Yuxuan [verfasserIn] Guan, Xiaoshu [verfasserIn] Sun, Huabin [verfasserIn] Bao, Yuequan [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Reliability engineering & system safety - London [u.a.] : Elsevier Science, 1988, 243 |
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Übergeordnetes Werk: |
volume:243 |
DOI / URN: |
10.1016/j.ress.2023.109841 |
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Katalog-ID: |
ELV066430488 |
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520 | |a Dominant failure modes (DFMs) of structural systems are integral to life prediction and reliability assessment. However, the computational efficiency of existing DFMs searching methods is constrained by neglecting the structural non-Euclidean properties. To break free from this shackle, this paper proposes a DFMs searching algorithm based on the graph neural network (GNN). The proposed algorithm can adaptively identify graph samples representing DFMs via completing graph classification. First, the target structural system is converted into an undirected graph to preserve its topological features. Second, a hierarchical graph attention mechanism is developed to establish the mapping relationship between structure intrinsic properties and DFMs. Finally, two adaptive sample selection strategies are devised to iteratively search DFMs and supplement graph datasets. In order to reduce the number of reliability analyses, the algorithm will terminate prematurely when unable to identify new DFMs. A 2D truss and a 3D frame are selected to test the computational efficiency and stability of the algorithm. The search results indicate that, despite providing different initial training sets, this GNN-based algorithm still converges to DFMs consistent with the result of Monte Carlo Simulation (MCS). Compared to the genetic algorithm (GA) and the β-unzipping method, the proposed algorithm exhibits higher computational efficiency. | ||
650 | 4 | |a Structural system | |
650 | 4 | |a Graph neural network | |
650 | 4 | |a Dominant failure mode | |
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700 | 1 | |a Guan, Xiaoshu |e verfasserin |4 aut | |
700 | 1 | |a Sun, Huabin |e verfasserin |0 (orcid)0000-0002-8831-7111 |4 aut | |
700 | 1 | |a Bao, Yuequan |e verfasserin |0 (orcid)0000-0001-7553-366X |4 aut | |
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10.1016/j.ress.2023.109841 doi (DE-627)ELV066430488 (ELSEVIER)S0951-8320(23)00755-X DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Tian, Yuxuan verfasserin aut An adaptive structural dominant failure modes searching method based on graph neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dominant failure modes (DFMs) of structural systems are integral to life prediction and reliability assessment. However, the computational efficiency of existing DFMs searching methods is constrained by neglecting the structural non-Euclidean properties. To break free from this shackle, this paper proposes a DFMs searching algorithm based on the graph neural network (GNN). The proposed algorithm can adaptively identify graph samples representing DFMs via completing graph classification. First, the target structural system is converted into an undirected graph to preserve its topological features. Second, a hierarchical graph attention mechanism is developed to establish the mapping relationship between structure intrinsic properties and DFMs. Finally, two adaptive sample selection strategies are devised to iteratively search DFMs and supplement graph datasets. In order to reduce the number of reliability analyses, the algorithm will terminate prematurely when unable to identify new DFMs. A 2D truss and a 3D frame are selected to test the computational efficiency and stability of the algorithm. The search results indicate that, despite providing different initial training sets, this GNN-based algorithm still converges to DFMs consistent with the result of Monte Carlo Simulation (MCS). Compared to the genetic algorithm (GA) and the β-unzipping method, the proposed algorithm exhibits higher computational efficiency. Structural system Graph neural network Dominant failure mode Deep learning Guan, Xiaoshu verfasserin aut Sun, Huabin verfasserin (orcid)0000-0002-8831-7111 aut Bao, Yuequan verfasserin (orcid)0000-0001-7553-366X aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 243 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 243 |
spelling |
10.1016/j.ress.2023.109841 doi (DE-627)ELV066430488 (ELSEVIER)S0951-8320(23)00755-X DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Tian, Yuxuan verfasserin aut An adaptive structural dominant failure modes searching method based on graph neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dominant failure modes (DFMs) of structural systems are integral to life prediction and reliability assessment. However, the computational efficiency of existing DFMs searching methods is constrained by neglecting the structural non-Euclidean properties. To break free from this shackle, this paper proposes a DFMs searching algorithm based on the graph neural network (GNN). The proposed algorithm can adaptively identify graph samples representing DFMs via completing graph classification. First, the target structural system is converted into an undirected graph to preserve its topological features. Second, a hierarchical graph attention mechanism is developed to establish the mapping relationship between structure intrinsic properties and DFMs. Finally, two adaptive sample selection strategies are devised to iteratively search DFMs and supplement graph datasets. In order to reduce the number of reliability analyses, the algorithm will terminate prematurely when unable to identify new DFMs. A 2D truss and a 3D frame are selected to test the computational efficiency and stability of the algorithm. The search results indicate that, despite providing different initial training sets, this GNN-based algorithm still converges to DFMs consistent with the result of Monte Carlo Simulation (MCS). Compared to the genetic algorithm (GA) and the β-unzipping method, the proposed algorithm exhibits higher computational efficiency. Structural system Graph neural network Dominant failure mode Deep learning Guan, Xiaoshu verfasserin aut Sun, Huabin verfasserin (orcid)0000-0002-8831-7111 aut Bao, Yuequan verfasserin (orcid)0000-0001-7553-366X aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 243 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 243 |
allfields_unstemmed |
10.1016/j.ress.2023.109841 doi (DE-627)ELV066430488 (ELSEVIER)S0951-8320(23)00755-X DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Tian, Yuxuan verfasserin aut An adaptive structural dominant failure modes searching method based on graph neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dominant failure modes (DFMs) of structural systems are integral to life prediction and reliability assessment. However, the computational efficiency of existing DFMs searching methods is constrained by neglecting the structural non-Euclidean properties. To break free from this shackle, this paper proposes a DFMs searching algorithm based on the graph neural network (GNN). The proposed algorithm can adaptively identify graph samples representing DFMs via completing graph classification. First, the target structural system is converted into an undirected graph to preserve its topological features. Second, a hierarchical graph attention mechanism is developed to establish the mapping relationship between structure intrinsic properties and DFMs. Finally, two adaptive sample selection strategies are devised to iteratively search DFMs and supplement graph datasets. In order to reduce the number of reliability analyses, the algorithm will terminate prematurely when unable to identify new DFMs. A 2D truss and a 3D frame are selected to test the computational efficiency and stability of the algorithm. The search results indicate that, despite providing different initial training sets, this GNN-based algorithm still converges to DFMs consistent with the result of Monte Carlo Simulation (MCS). Compared to the genetic algorithm (GA) and the β-unzipping method, the proposed algorithm exhibits higher computational efficiency. Structural system Graph neural network Dominant failure mode Deep learning Guan, Xiaoshu verfasserin aut Sun, Huabin verfasserin (orcid)0000-0002-8831-7111 aut Bao, Yuequan verfasserin (orcid)0000-0001-7553-366X aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 243 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 243 |
allfieldsGer |
10.1016/j.ress.2023.109841 doi (DE-627)ELV066430488 (ELSEVIER)S0951-8320(23)00755-X DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Tian, Yuxuan verfasserin aut An adaptive structural dominant failure modes searching method based on graph neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dominant failure modes (DFMs) of structural systems are integral to life prediction and reliability assessment. However, the computational efficiency of existing DFMs searching methods is constrained by neglecting the structural non-Euclidean properties. To break free from this shackle, this paper proposes a DFMs searching algorithm based on the graph neural network (GNN). The proposed algorithm can adaptively identify graph samples representing DFMs via completing graph classification. First, the target structural system is converted into an undirected graph to preserve its topological features. Second, a hierarchical graph attention mechanism is developed to establish the mapping relationship between structure intrinsic properties and DFMs. Finally, two adaptive sample selection strategies are devised to iteratively search DFMs and supplement graph datasets. In order to reduce the number of reliability analyses, the algorithm will terminate prematurely when unable to identify new DFMs. A 2D truss and a 3D frame are selected to test the computational efficiency and stability of the algorithm. The search results indicate that, despite providing different initial training sets, this GNN-based algorithm still converges to DFMs consistent with the result of Monte Carlo Simulation (MCS). Compared to the genetic algorithm (GA) and the β-unzipping method, the proposed algorithm exhibits higher computational efficiency. Structural system Graph neural network Dominant failure mode Deep learning Guan, Xiaoshu verfasserin aut Sun, Huabin verfasserin (orcid)0000-0002-8831-7111 aut Bao, Yuequan verfasserin (orcid)0000-0001-7553-366X aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 243 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 243 |
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10.1016/j.ress.2023.109841 doi (DE-627)ELV066430488 (ELSEVIER)S0951-8320(23)00755-X DE-627 ger DE-627 rda eng 600 VZ 50.16 bkl 85.38 bkl Tian, Yuxuan verfasserin aut An adaptive structural dominant failure modes searching method based on graph neural network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dominant failure modes (DFMs) of structural systems are integral to life prediction and reliability assessment. However, the computational efficiency of existing DFMs searching methods is constrained by neglecting the structural non-Euclidean properties. To break free from this shackle, this paper proposes a DFMs searching algorithm based on the graph neural network (GNN). The proposed algorithm can adaptively identify graph samples representing DFMs via completing graph classification. First, the target structural system is converted into an undirected graph to preserve its topological features. Second, a hierarchical graph attention mechanism is developed to establish the mapping relationship between structure intrinsic properties and DFMs. Finally, two adaptive sample selection strategies are devised to iteratively search DFMs and supplement graph datasets. In order to reduce the number of reliability analyses, the algorithm will terminate prematurely when unable to identify new DFMs. A 2D truss and a 3D frame are selected to test the computational efficiency and stability of the algorithm. The search results indicate that, despite providing different initial training sets, this GNN-based algorithm still converges to DFMs consistent with the result of Monte Carlo Simulation (MCS). Compared to the genetic algorithm (GA) and the β-unzipping method, the proposed algorithm exhibits higher computational efficiency. Structural system Graph neural network Dominant failure mode Deep learning Guan, Xiaoshu verfasserin aut Sun, Huabin verfasserin (orcid)0000-0002-8831-7111 aut Bao, Yuequan verfasserin (orcid)0000-0001-7553-366X aut Enthalten in Reliability engineering & system safety London [u.a.] : Elsevier Science, 1988 243 Online-Ressource (DE-627)320608743 (DE-600)2021091-7 (DE-576)259485217 0951-8320 nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 50.16 Technische Zuverlässigkeit Instandhaltung VZ 85.38 Qualitätsmanagement VZ AR 243 |
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600 VZ 50.16 bkl 85.38 bkl An adaptive structural dominant failure modes searching method based on graph neural network Structural system Graph neural network Dominant failure mode Deep learning |
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ddc 600 bkl 50.16 bkl 85.38 misc Structural system misc Graph neural network misc Dominant failure mode misc Deep learning |
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ddc 600 bkl 50.16 bkl 85.38 misc Structural system misc Graph neural network misc Dominant failure mode misc Deep learning |
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ddc 600 bkl 50.16 bkl 85.38 misc Structural system misc Graph neural network misc Dominant failure mode misc Deep learning |
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An adaptive structural dominant failure modes searching method based on graph neural network |
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An adaptive structural dominant failure modes searching method based on graph neural network |
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Tian, Yuxuan Guan, Xiaoshu Sun, Huabin Bao, Yuequan |
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an adaptive structural dominant failure modes searching method based on graph neural network |
title_auth |
An adaptive structural dominant failure modes searching method based on graph neural network |
abstract |
Dominant failure modes (DFMs) of structural systems are integral to life prediction and reliability assessment. However, the computational efficiency of existing DFMs searching methods is constrained by neglecting the structural non-Euclidean properties. To break free from this shackle, this paper proposes a DFMs searching algorithm based on the graph neural network (GNN). The proposed algorithm can adaptively identify graph samples representing DFMs via completing graph classification. First, the target structural system is converted into an undirected graph to preserve its topological features. Second, a hierarchical graph attention mechanism is developed to establish the mapping relationship between structure intrinsic properties and DFMs. Finally, two adaptive sample selection strategies are devised to iteratively search DFMs and supplement graph datasets. In order to reduce the number of reliability analyses, the algorithm will terminate prematurely when unable to identify new DFMs. A 2D truss and a 3D frame are selected to test the computational efficiency and stability of the algorithm. The search results indicate that, despite providing different initial training sets, this GNN-based algorithm still converges to DFMs consistent with the result of Monte Carlo Simulation (MCS). Compared to the genetic algorithm (GA) and the β-unzipping method, the proposed algorithm exhibits higher computational efficiency. |
abstractGer |
Dominant failure modes (DFMs) of structural systems are integral to life prediction and reliability assessment. However, the computational efficiency of existing DFMs searching methods is constrained by neglecting the structural non-Euclidean properties. To break free from this shackle, this paper proposes a DFMs searching algorithm based on the graph neural network (GNN). The proposed algorithm can adaptively identify graph samples representing DFMs via completing graph classification. First, the target structural system is converted into an undirected graph to preserve its topological features. Second, a hierarchical graph attention mechanism is developed to establish the mapping relationship between structure intrinsic properties and DFMs. Finally, two adaptive sample selection strategies are devised to iteratively search DFMs and supplement graph datasets. In order to reduce the number of reliability analyses, the algorithm will terminate prematurely when unable to identify new DFMs. A 2D truss and a 3D frame are selected to test the computational efficiency and stability of the algorithm. The search results indicate that, despite providing different initial training sets, this GNN-based algorithm still converges to DFMs consistent with the result of Monte Carlo Simulation (MCS). Compared to the genetic algorithm (GA) and the β-unzipping method, the proposed algorithm exhibits higher computational efficiency. |
abstract_unstemmed |
Dominant failure modes (DFMs) of structural systems are integral to life prediction and reliability assessment. However, the computational efficiency of existing DFMs searching methods is constrained by neglecting the structural non-Euclidean properties. To break free from this shackle, this paper proposes a DFMs searching algorithm based on the graph neural network (GNN). The proposed algorithm can adaptively identify graph samples representing DFMs via completing graph classification. First, the target structural system is converted into an undirected graph to preserve its topological features. Second, a hierarchical graph attention mechanism is developed to establish the mapping relationship between structure intrinsic properties and DFMs. Finally, two adaptive sample selection strategies are devised to iteratively search DFMs and supplement graph datasets. In order to reduce the number of reliability analyses, the algorithm will terminate prematurely when unable to identify new DFMs. A 2D truss and a 3D frame are selected to test the computational efficiency and stability of the algorithm. The search results indicate that, despite providing different initial training sets, this GNN-based algorithm still converges to DFMs consistent with the result of Monte Carlo Simulation (MCS). Compared to the genetic algorithm (GA) and the β-unzipping method, the proposed algorithm exhibits higher computational efficiency. |
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