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Author:

Zheng, Ruihao (Zheng, Ruihao.) | Xiong, Chen (Xiong, Chen.) | Deng, Xiangbin (Deng, Xiangbin.) | Li, Qiangsheng (Li, Qiangsheng.) | Li, Yi (Li, Yi.) (Scholars:李易)

Indexed by:

SCIE

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-2 = 0.8737) compared with that of the BPNN (R-2 = 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.

Keyword:

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

Author Community:

  • [ 1 ] [Zheng, Ruihao]Shenzhen Univ, Guangdong Prov Key Lab Durabil Marine Civil Engn, Shenzhen 518060, Peoples R China
  • [ 2 ] [Xiong, Chen]Shenzhen Univ, Guangdong Prov Key Lab Durabil Marine Civil Engn, Shenzhen 518060, Peoples R China
  • [ 3 ] [Deng, Xiangbin]Shenzhen Univ, Guangdong Prov Key Lab Durabil Marine Civil Engn, Shenzhen 518060, Peoples R China
  • [ 4 ] [Li, Qiangsheng]Shenzhen Univ, Guangdong Prov Key Lab Durabil Marine Civil Engn, Shenzhen 518060, Peoples R China
  • [ 5 ] [Li, Yi]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Xiong, Chen]Shenzhen Univ, Guangdong Prov Key Lab Durabil Marine Civil Engn, Shenzhen 518060, Peoples R China

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Source :

APPLIED SCIENCES-BASEL

Year: 2020

Issue: 18

Volume: 10

2 . 7 0 0

JCR@2022

ESI Discipline: ENGINEERING;

ESI HC Threshold:115

Cited Count:

WoS CC Cited Count: 10

SCOPUS Cited Count: 10

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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