Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods

This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algori...
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Gespeichert in:

Autor*in:	Ruihao Zheng [verfasserIn] Chen Xiong [verfasserIn] Xiangbin Deng [verfasserIn] Qiangsheng Li [verfasserIn] Yi Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	machine learning backpropagation neural network convolutional neural network seismic damage simulation time-history analysis earthquake destructive power

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 10(2020), 18, p 6210
Übergeordnetes Werk:	volume:10 ; year:2020 ; number:18, p 6210

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app10186210

Katalog-ID:	DOAJ077536746

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520			\|a This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R<sup<2</sup< = 0.8737) compared with that of the BPNN (R<sup<2</sup< = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake.
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allfields	10.3390/app10186210 doi (DE-627)DOAJ077536746 (DE-599)DOAJ7c7cb6226dd046d5b40b7a11b343a919 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Ruihao Zheng verfasserin aut Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R<sup<2</sup< = 0.8737) compared with that of the BPNN (R<sup<2</sup< = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake. machine learning backpropagation neural network convolutional neural network seismic damage simulation time-history analysis earthquake destructive power Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Chen Xiong verfasserin aut Xiangbin Deng verfasserin aut Qiangsheng Li verfasserin aut Yi Li verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 18, p 6210 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:18, p 6210 https://doi.org/10.3390/app10186210 kostenfrei https://doaj.org/article/7c7cb6226dd046d5b40b7a11b343a919 kostenfrei https://www.mdpi.com/2076-3417/10/18/6210 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 18, p 6210
spelling	10.3390/app10186210 doi (DE-627)DOAJ077536746 (DE-599)DOAJ7c7cb6226dd046d5b40b7a11b343a919 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Ruihao Zheng verfasserin aut Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R<sup<2</sup< = 0.8737) compared with that of the BPNN (R<sup<2</sup< = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake. machine learning backpropagation neural network convolutional neural network seismic damage simulation time-history analysis earthquake destructive power Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Chen Xiong verfasserin aut Xiangbin Deng verfasserin aut Qiangsheng Li verfasserin aut Yi Li verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 18, p 6210 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:18, p 6210 https://doi.org/10.3390/app10186210 kostenfrei https://doaj.org/article/7c7cb6226dd046d5b40b7a11b343a919 kostenfrei https://www.mdpi.com/2076-3417/10/18/6210 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 18, p 6210
allfields_unstemmed	10.3390/app10186210 doi (DE-627)DOAJ077536746 (DE-599)DOAJ7c7cb6226dd046d5b40b7a11b343a919 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Ruihao Zheng verfasserin aut Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R<sup<2</sup< = 0.8737) compared with that of the BPNN (R<sup<2</sup< = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake. machine learning backpropagation neural network convolutional neural network seismic damage simulation time-history analysis earthquake destructive power Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Chen Xiong verfasserin aut Xiangbin Deng verfasserin aut Qiangsheng Li verfasserin aut Yi Li verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 18, p 6210 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:18, p 6210 https://doi.org/10.3390/app10186210 kostenfrei https://doaj.org/article/7c7cb6226dd046d5b40b7a11b343a919 kostenfrei https://www.mdpi.com/2076-3417/10/18/6210 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 18, p 6210
allfieldsGer	10.3390/app10186210 doi (DE-627)DOAJ077536746 (DE-599)DOAJ7c7cb6226dd046d5b40b7a11b343a919 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Ruihao Zheng verfasserin aut Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R<sup<2</sup< = 0.8737) compared with that of the BPNN (R<sup<2</sup< = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake. machine learning backpropagation neural network convolutional neural network seismic damage simulation time-history analysis earthquake destructive power Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Chen Xiong verfasserin aut Xiangbin Deng verfasserin aut Qiangsheng Li verfasserin aut Yi Li verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 18, p 6210 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:18, p 6210 https://doi.org/10.3390/app10186210 kostenfrei https://doaj.org/article/7c7cb6226dd046d5b40b7a11b343a919 kostenfrei https://www.mdpi.com/2076-3417/10/18/6210 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 18, p 6210
allfieldsSound	10.3390/app10186210 doi (DE-627)DOAJ077536746 (DE-599)DOAJ7c7cb6226dd046d5b40b7a11b343a919 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Ruihao Zheng verfasserin aut Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R<sup<2</sup< = 0.8737) compared with that of the BPNN (R<sup<2</sup< = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake. machine learning backpropagation neural network convolutional neural network seismic damage simulation time-history analysis earthquake destructive power Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Chen Xiong verfasserin aut Xiangbin Deng verfasserin aut Qiangsheng Li verfasserin aut Yi Li verfasserin aut In Applied Sciences MDPI AG, 2012 10(2020), 18, p 6210 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:10 year:2020 number:18, p 6210 https://doi.org/10.3390/app10186210 kostenfrei https://doaj.org/article/7c7cb6226dd046d5b40b7a11b343a919 kostenfrei https://www.mdpi.com/2076-3417/10/18/6210 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 18, p 6210
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callnumber-first	T - Technology
author	Ruihao Zheng
spellingShingle	Ruihao Zheng misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc machine learning misc backpropagation neural network misc convolutional neural network misc seismic damage simulation misc time-history analysis misc earthquake destructive power misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods machine learning backpropagation neural network convolutional neural network seismic damage simulation time-history analysis earthquake destructive power
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc machine learning misc backpropagation neural network misc convolutional neural network misc seismic damage simulation misc time-history analysis misc earthquake destructive power misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
topic_unstemmed	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc machine learning misc backpropagation neural network misc convolutional neural network misc seismic damage simulation misc time-history analysis misc earthquake destructive power misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
topic_browse	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc machine learning misc backpropagation neural network misc convolutional neural network misc seismic damage simulation misc time-history analysis misc earthquake destructive power misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
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title_full	Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
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title_sort	assessment of earthquake destructive power to structures based on machine learning methods
callnumber	TA1-2040
title_auth	Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
abstract	This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R<sup<2</sup< = 0.8737) compared with that of the BPNN (R<sup<2</sup< = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake.
abstractGer	This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R<sup<2</sup< = 0.8737) compared with that of the BPNN (R<sup<2</sup< = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake.
abstract_unstemmed	This study presents a machine learning-based method for the destructive power assessment of earthquake to structures. First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R<sup<2</sup< = 0.8737) compared with that of the BPNN (R<sup<2</sup< = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake.
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title_short	Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
url	https://doi.org/10.3390/app10186210 https://doaj.org/article/7c7cb6226dd046d5b40b7a11b343a919 https://www.mdpi.com/2076-3417/10/18/6210 https://doaj.org/toc/2076-3417
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author2	Chen Xiong Xiangbin Deng Qiangsheng Li Yi Li
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First, the analysis procedure of the method is presented, and the backpropagation neural network (BPNN) and convolutional neural network (CNN) are used as the machine learning algorithms. Second, the optimized BPNN architecture is obtained by discussing the influence of a different number of hidden layers and nodes. Third, the CNN architecture is proposed based on several classical deep learning networks. To build the machine learning models, 50,570 time-history analysis results of a structural system subjected to different ground motions are used as training, validation, and test samples. The results of the BPNN indicate that the features extraction method based on the short-time Fourier transform (STFT) can well reflect the frequency-/time-domain characteristics of ground motions. The results of the CNN indicate that the CNN exhibits better accuracy (R<sup<2</sup< = 0.8737) compared with that of the BPNN (R<sup<2</sup< = 0.6784). Furthermore, the CNN model exhibits remarkable computational efficiency, the prediction of 1000 structures based on the CNN model takes 0.762 s, while 507.81 s are required for the conventional time-history analysis (THA)-based simulation. Feature visualization of different layers of the CNN reveals that the shallow to deep layers of the CNN can extract the high to low-frequency features of ground motions. The proposed method can assist in the fast prediction of engineering demand parameters of large-number structures, which facilitates the damage or loss assessments of regional structures for timely emergency response and disaster relief after earthquake.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">backpropagation neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">convolutional neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">seismic damage simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">time-history analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">earthquake destructive power</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering (General). 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Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods

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