Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis
This paper is devoted to the research on applying the deep learning method to nonlinear structural post-disaster damage state assessment. Transformer and Informer networks with a classification network customized according to the adopted damage assessment framework are proposed for data-driven struc...
Ausführliche Beschreibung
Autor*in: |
Chen, Youjun [verfasserIn] Sun, Zeyang [verfasserIn] Zhang, Ruiyang [verfasserIn] Yao, Liuzhen [verfasserIn] Wu, Gang [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Schlagwörter: |
Structural response prediction Assessment framework on structural resilience |
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Übergeordnetes Werk: |
Enthalten in: Computers & structures - Amsterdam [u.a.] : Elsevier, 1971, 281 |
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Übergeordnetes Werk: |
volume:281 |
DOI / URN: |
10.1016/j.compstruc.2023.107038 |
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Katalog-ID: |
ELV00954769X |
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520 | |a This paper is devoted to the research on applying the deep learning method to nonlinear structural post-disaster damage state assessment. Transformer and Informer networks with a classification network customized according to the adopted damage assessment framework are proposed for data-driven structural seismic response and damage state modeling. Compared with recurrent neural network and convolution neural network, the networks in this paper can predict the elastoplastic response of nonlinear structures more effectively. In addition, this paper presents a method for rapid structural fragility analysis, which can consider multiple damage assessment indexes at the same time. The performance of the proposed approach is successfully demonstrated through two examples, including a numerical analysis validation and a field sensing validation. The results show that the Transformer network used in this paper is a reliable and computationally efficient approach for predicting the structural seismic response and damage category, and appears great potential in structural health monitoring and rapid assessment on post-disaster structural resilience. | ||
650 | 4 | |a Structural response prediction | |
650 | 4 | |a Damage category prediction | |
650 | 4 | |a Assessment framework on structural resilience | |
650 | 4 | |a Structural rapid fragility analysis | |
650 | 4 | |a Attention mechanism | |
700 | 1 | |a Sun, Zeyang |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Ruiyang |e verfasserin |4 aut | |
700 | 1 | |a Yao, Liuzhen |e verfasserin |4 aut | |
700 | 1 | |a Wu, Gang |e verfasserin |4 aut | |
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10.1016/j.compstruc.2023.107038 doi (DE-627)ELV00954769X (ELSEVIER)S0045-7949(23)00068-8 DE-627 ger DE-627 rda eng 004 DE-600 50.03 bkl 50.31 bkl Chen, Youjun verfasserin aut Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper is devoted to the research on applying the deep learning method to nonlinear structural post-disaster damage state assessment. Transformer and Informer networks with a classification network customized according to the adopted damage assessment framework are proposed for data-driven structural seismic response and damage state modeling. Compared with recurrent neural network and convolution neural network, the networks in this paper can predict the elastoplastic response of nonlinear structures more effectively. In addition, this paper presents a method for rapid structural fragility analysis, which can consider multiple damage assessment indexes at the same time. The performance of the proposed approach is successfully demonstrated through two examples, including a numerical analysis validation and a field sensing validation. The results show that the Transformer network used in this paper is a reliable and computationally efficient approach for predicting the structural seismic response and damage category, and appears great potential in structural health monitoring and rapid assessment on post-disaster structural resilience. Structural response prediction Damage category prediction Assessment framework on structural resilience Structural rapid fragility analysis Attention mechanism Sun, Zeyang verfasserin aut Zhang, Ruiyang verfasserin aut Yao, Liuzhen verfasserin aut Wu, Gang verfasserin aut Enthalten in Computers & structures Amsterdam [u.a.] : Elsevier, 1971 281 Online-Ressource (DE-627)31950834X (DE-600)2013155-0 (DE-576)094531439 0045-7949 nnns volume:281 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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 50.03 Methoden und Techniken der Ingenieurwissenschaften 50.31 Technische Mechanik AR 281 |
spelling |
10.1016/j.compstruc.2023.107038 doi (DE-627)ELV00954769X (ELSEVIER)S0045-7949(23)00068-8 DE-627 ger DE-627 rda eng 004 DE-600 50.03 bkl 50.31 bkl Chen, Youjun verfasserin aut Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper is devoted to the research on applying the deep learning method to nonlinear structural post-disaster damage state assessment. Transformer and Informer networks with a classification network customized according to the adopted damage assessment framework are proposed for data-driven structural seismic response and damage state modeling. Compared with recurrent neural network and convolution neural network, the networks in this paper can predict the elastoplastic response of nonlinear structures more effectively. In addition, this paper presents a method for rapid structural fragility analysis, which can consider multiple damage assessment indexes at the same time. The performance of the proposed approach is successfully demonstrated through two examples, including a numerical analysis validation and a field sensing validation. The results show that the Transformer network used in this paper is a reliable and computationally efficient approach for predicting the structural seismic response and damage category, and appears great potential in structural health monitoring and rapid assessment on post-disaster structural resilience. Structural response prediction Damage category prediction Assessment framework on structural resilience Structural rapid fragility analysis Attention mechanism Sun, Zeyang verfasserin aut Zhang, Ruiyang verfasserin aut Yao, Liuzhen verfasserin aut Wu, Gang verfasserin aut Enthalten in Computers & structures Amsterdam [u.a.] : Elsevier, 1971 281 Online-Ressource (DE-627)31950834X (DE-600)2013155-0 (DE-576)094531439 0045-7949 nnns volume:281 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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 50.03 Methoden und Techniken der Ingenieurwissenschaften 50.31 Technische Mechanik AR 281 |
allfields_unstemmed |
10.1016/j.compstruc.2023.107038 doi (DE-627)ELV00954769X (ELSEVIER)S0045-7949(23)00068-8 DE-627 ger DE-627 rda eng 004 DE-600 50.03 bkl 50.31 bkl Chen, Youjun verfasserin aut Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper is devoted to the research on applying the deep learning method to nonlinear structural post-disaster damage state assessment. Transformer and Informer networks with a classification network customized according to the adopted damage assessment framework are proposed for data-driven structural seismic response and damage state modeling. Compared with recurrent neural network and convolution neural network, the networks in this paper can predict the elastoplastic response of nonlinear structures more effectively. In addition, this paper presents a method for rapid structural fragility analysis, which can consider multiple damage assessment indexes at the same time. The performance of the proposed approach is successfully demonstrated through two examples, including a numerical analysis validation and a field sensing validation. The results show that the Transformer network used in this paper is a reliable and computationally efficient approach for predicting the structural seismic response and damage category, and appears great potential in structural health monitoring and rapid assessment on post-disaster structural resilience. Structural response prediction Damage category prediction Assessment framework on structural resilience Structural rapid fragility analysis Attention mechanism Sun, Zeyang verfasserin aut Zhang, Ruiyang verfasserin aut Yao, Liuzhen verfasserin aut Wu, Gang verfasserin aut Enthalten in Computers & structures Amsterdam [u.a.] : Elsevier, 1971 281 Online-Ressource (DE-627)31950834X (DE-600)2013155-0 (DE-576)094531439 0045-7949 nnns volume:281 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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 50.03 Methoden und Techniken der Ingenieurwissenschaften 50.31 Technische Mechanik AR 281 |
allfieldsGer |
10.1016/j.compstruc.2023.107038 doi (DE-627)ELV00954769X (ELSEVIER)S0045-7949(23)00068-8 DE-627 ger DE-627 rda eng 004 DE-600 50.03 bkl 50.31 bkl Chen, Youjun verfasserin aut Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper is devoted to the research on applying the deep learning method to nonlinear structural post-disaster damage state assessment. Transformer and Informer networks with a classification network customized according to the adopted damage assessment framework are proposed for data-driven structural seismic response and damage state modeling. Compared with recurrent neural network and convolution neural network, the networks in this paper can predict the elastoplastic response of nonlinear structures more effectively. In addition, this paper presents a method for rapid structural fragility analysis, which can consider multiple damage assessment indexes at the same time. The performance of the proposed approach is successfully demonstrated through two examples, including a numerical analysis validation and a field sensing validation. The results show that the Transformer network used in this paper is a reliable and computationally efficient approach for predicting the structural seismic response and damage category, and appears great potential in structural health monitoring and rapid assessment on post-disaster structural resilience. Structural response prediction Damage category prediction Assessment framework on structural resilience Structural rapid fragility analysis Attention mechanism Sun, Zeyang verfasserin aut Zhang, Ruiyang verfasserin aut Yao, Liuzhen verfasserin aut Wu, Gang verfasserin aut Enthalten in Computers & structures Amsterdam [u.a.] : Elsevier, 1971 281 Online-Ressource (DE-627)31950834X (DE-600)2013155-0 (DE-576)094531439 0045-7949 nnns volume:281 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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 50.03 Methoden und Techniken der Ingenieurwissenschaften 50.31 Technische Mechanik AR 281 |
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10.1016/j.compstruc.2023.107038 doi (DE-627)ELV00954769X (ELSEVIER)S0045-7949(23)00068-8 DE-627 ger DE-627 rda eng 004 DE-600 50.03 bkl 50.31 bkl Chen, Youjun verfasserin aut Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper is devoted to the research on applying the deep learning method to nonlinear structural post-disaster damage state assessment. Transformer and Informer networks with a classification network customized according to the adopted damage assessment framework are proposed for data-driven structural seismic response and damage state modeling. Compared with recurrent neural network and convolution neural network, the networks in this paper can predict the elastoplastic response of nonlinear structures more effectively. In addition, this paper presents a method for rapid structural fragility analysis, which can consider multiple damage assessment indexes at the same time. The performance of the proposed approach is successfully demonstrated through two examples, including a numerical analysis validation and a field sensing validation. The results show that the Transformer network used in this paper is a reliable and computationally efficient approach for predicting the structural seismic response and damage category, and appears great potential in structural health monitoring and rapid assessment on post-disaster structural resilience. Structural response prediction Damage category prediction Assessment framework on structural resilience Structural rapid fragility analysis Attention mechanism Sun, Zeyang verfasserin aut Zhang, Ruiyang verfasserin aut Yao, Liuzhen verfasserin aut Wu, Gang verfasserin aut Enthalten in Computers & structures Amsterdam [u.a.] : Elsevier, 1971 281 Online-Ressource (DE-627)31950834X (DE-600)2013155-0 (DE-576)094531439 0045-7949 nnns volume:281 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_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_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 50.03 Methoden und Techniken der Ingenieurwissenschaften 50.31 Technische Mechanik AR 281 |
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Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis |
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Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis |
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Chen, Youjun |
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Chen, Youjun Sun, Zeyang Zhang, Ruiyang Yao, Liuzhen Wu, Gang |
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Chen, Youjun |
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10.1016/j.compstruc.2023.107038 |
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attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis |
title_auth |
Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis |
abstract |
This paper is devoted to the research on applying the deep learning method to nonlinear structural post-disaster damage state assessment. Transformer and Informer networks with a classification network customized according to the adopted damage assessment framework are proposed for data-driven structural seismic response and damage state modeling. Compared with recurrent neural network and convolution neural network, the networks in this paper can predict the elastoplastic response of nonlinear structures more effectively. In addition, this paper presents a method for rapid structural fragility analysis, which can consider multiple damage assessment indexes at the same time. The performance of the proposed approach is successfully demonstrated through two examples, including a numerical analysis validation and a field sensing validation. The results show that the Transformer network used in this paper is a reliable and computationally efficient approach for predicting the structural seismic response and damage category, and appears great potential in structural health monitoring and rapid assessment on post-disaster structural resilience. |
abstractGer |
This paper is devoted to the research on applying the deep learning method to nonlinear structural post-disaster damage state assessment. Transformer and Informer networks with a classification network customized according to the adopted damage assessment framework are proposed for data-driven structural seismic response and damage state modeling. Compared with recurrent neural network and convolution neural network, the networks in this paper can predict the elastoplastic response of nonlinear structures more effectively. In addition, this paper presents a method for rapid structural fragility analysis, which can consider multiple damage assessment indexes at the same time. The performance of the proposed approach is successfully demonstrated through two examples, including a numerical analysis validation and a field sensing validation. The results show that the Transformer network used in this paper is a reliable and computationally efficient approach for predicting the structural seismic response and damage category, and appears great potential in structural health monitoring and rapid assessment on post-disaster structural resilience. |
abstract_unstemmed |
This paper is devoted to the research on applying the deep learning method to nonlinear structural post-disaster damage state assessment. Transformer and Informer networks with a classification network customized according to the adopted damage assessment framework are proposed for data-driven structural seismic response and damage state modeling. Compared with recurrent neural network and convolution neural network, the networks in this paper can predict the elastoplastic response of nonlinear structures more effectively. In addition, this paper presents a method for rapid structural fragility analysis, which can consider multiple damage assessment indexes at the same time. The performance of the proposed approach is successfully demonstrated through two examples, including a numerical analysis validation and a field sensing validation. The results show that the Transformer network used in this paper is a reliable and computationally efficient approach for predicting the structural seismic response and damage category, and appears great potential in structural health monitoring and rapid assessment on post-disaster structural resilience. |
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Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis |
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Sun, Zeyang Zhang, Ruiyang Yao, Liuzhen Wu, Gang |
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