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

Gespeichert in:

Autor*in:	Chen, Youjun [verfasserIn] Sun, Zeyang [verfasserIn] Zhang, Ruiyang [verfasserIn] Yao, Liuzhen [verfasserIn] Wu, Gang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Structural response prediction Damage category prediction Assessment framework on structural resilience Structural rapid fragility analysis Attention mechanism

Übergeordnetes Werk:	Enthalten in: Computers & structures - Amsterdam [u.a.] : Elsevier, 1971, 281
Übergeordnetes Werk:	volume:281

DOI / URN:	10.1016/j.compstruc.2023.107038

Katalog-ID:	ELV00954769X

Internformat


LEADER	01000caa a22002652 4500
001	ELV00954769X
003	DE-627
005	20230524143255.0
007	cr uuu---uuuuu
008	230511s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.compstruc.2023.107038 \|2 doi
035			\|a (DE-627)ELV00954769X
035			\|a (ELSEVIER)S0045-7949(23)00068-8
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 004 \|q DE-600
084			\|a 50.03 \|2 bkl
084			\|a 50.31 \|2 bkl
100	1		\|a Chen, Youjun \|e verfasserin \|4 aut
245	1	0	\|a Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis
264		1	\|c 2023
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
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
773	0	8	\|i Enthalten in \|t Computers & structures \|d Amsterdam [u.a.] : Elsevier, 1971 \|g 281 \|h Online-Ressource \|w (DE-627)31950834X \|w (DE-600)2013155-0 \|w (DE-576)094531439 \|x 0045-7949 \|7 nnns
773	1	8	\|g volume:281
912			\|a GBV_USEFLAG_U
912			\|a SYSFLAG_U
912			\|a GBV_ELV
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
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_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
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_2007
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_2034
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_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 50.03 \|j Methoden und Techniken der Ingenieurwissenschaften
936	b	k	\|a 50.31 \|j Technische Mechanik
951			\|a AR
952			\|d 281

Indexfelder

author_variant	y c yc z s zs r z rz l y ly g w gw
matchkey_str	article:00457949:2023----::tetomcaimaenuantokfrtutrlotatqaeaaettpei
hierarchy_sort_str	2023
bklnumber	50.03 50.31
publishDate	2023
allfields	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
allfieldsSound	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
language	English
source	Enthalten in Computers & structures 281 volume:281
sourceStr	Enthalten in Computers & structures 281 volume:281
format_phy_str_mv	Article
bklname	Methoden und Techniken der Ingenieurwissenschaften Technische Mechanik
institution	findex.gbv.de
topic_facet	Structural response prediction Damage category prediction Assessment framework on structural resilience Structural rapid fragility analysis Attention mechanism
dewey-raw	004
isfreeaccess_bool	false
container_title	Computers & structures
authorswithroles_txt_mv	Chen, Youjun @@aut@@ Sun, Zeyang @@aut@@ Zhang, Ruiyang @@aut@@ Yao, Liuzhen @@aut@@ Wu, Gang @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	31950834X
dewey-sort	14
id	ELV00954769X
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV00954769X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524143255.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230511s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.compstruc.2023.107038</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV00954769X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0045-7949(23)00068-8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.03</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.31</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chen, Youjun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Structural response prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Damage category prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Assessment framework on structural resilience</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Structural rapid fragility analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Attention mechanism</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Zeyang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Ruiyang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yao, Liuzhen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Gang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computers & structures</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier, 1971</subfield><subfield code="g">281</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)31950834X</subfield><subfield code="w">(DE-600)2013155-0</subfield><subfield code="w">(DE-576)094531439</subfield><subfield code="x">0045-7949</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.03</subfield><subfield code="j">Methoden und Techniken der Ingenieurwissenschaften</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.31</subfield><subfield code="j">Technische Mechanik</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">281</subfield></datafield></record></collection>
author	Chen, Youjun
spellingShingle	Chen, Youjun ddc 004 bkl 50.03 bkl 50.31 misc Structural response prediction misc Damage category prediction misc Assessment framework on structural resilience misc Structural rapid fragility analysis misc Attention mechanism Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis
authorStr	Chen, Youjun
ppnlink_with_tag_str_mv	@@773@@(DE-627)31950834X
format	electronic Article
dewey-ones	004 - Data processing & computer science
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
issn	0045-7949
topic_title	004 DE-600 50.03 bkl 50.31 bkl Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis Structural response prediction Damage category prediction Assessment framework on structural resilience Structural rapid fragility analysis Attention mechanism
topic	ddc 004 bkl 50.03 bkl 50.31 misc Structural response prediction misc Damage category prediction misc Assessment framework on structural resilience misc Structural rapid fragility analysis misc Attention mechanism
topic_unstemmed	ddc 004 bkl 50.03 bkl 50.31 misc Structural response prediction misc Damage category prediction misc Assessment framework on structural resilience misc Structural rapid fragility analysis misc Attention mechanism
topic_browse	ddc 004 bkl 50.03 bkl 50.31 misc Structural response prediction misc Damage category prediction misc Assessment framework on structural resilience misc Structural rapid fragility analysis misc Attention mechanism
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Computers & structures
hierarchy_parent_id	31950834X
dewey-tens	000 - Computer science, knowledge & systems
hierarchy_top_title	Computers & structures
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)31950834X (DE-600)2013155-0 (DE-576)094531439
title	Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis
ctrlnum	(DE-627)ELV00954769X (ELSEVIER)S0045-7949(23)00068-8
title_full	Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis
author_sort	Chen, Youjun
journal	Computers & structures
journalStr	Computers & structures
lang_code	eng
isOA_bool	false
dewey-hundreds	000 - Computer science, information & general works
recordtype	marc
publishDateSort	2023
contenttype_str_mv	zzz
author_browse	Chen, Youjun Sun, Zeyang Zhang, Ruiyang Yao, Liuzhen Wu, Gang
container_volume	281
class	004 DE-600 50.03 bkl 50.31 bkl
format_se	Elektronische Aufsätze
author-letter	Chen, Youjun
doi_str_mv	10.1016/j.compstruc.2023.107038
dewey-full	004
author2-role	verfasserin
title_sort	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.
collection_details	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
title_short	Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis
remote_bool	true
author2	Sun, Zeyang Zhang, Ruiyang Yao, Liuzhen Wu, Gang
author2Str	Sun, Zeyang Zhang, Ruiyang Yao, Liuzhen Wu, Gang
ppnlink	31950834X
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.compstruc.2023.107038
up_date	2024-07-06T23:32:10.241Z
_version_	1803874447123283968
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV00954769X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524143255.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230511s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.compstruc.2023.107038</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV00954769X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0045-7949(23)00068-8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.03</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.31</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chen, Youjun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Structural response prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Damage category prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Assessment framework on structural resilience</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Structural rapid fragility analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Attention mechanism</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Zeyang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Ruiyang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yao, Liuzhen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Gang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computers & structures</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier, 1971</subfield><subfield code="g">281</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)31950834X</subfield><subfield code="w">(DE-600)2013155-0</subfield><subfield code="w">(DE-576)094531439</subfield><subfield code="x">0045-7949</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_101</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.03</subfield><subfield code="j">Methoden und Techniken der Ingenieurwissenschaften</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.31</subfield><subfield code="j">Technische Mechanik</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">281</subfield></datafield></record></collection>
score	7.4007607

Nicht das Richtige dabei?

Schreiben Sie uns!

Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?