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Near-fault ground motion synthesis based on conditional generation adversarial network.

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Title: Near-fault ground motion synthesis based on conditional generation adversarial network.
Authors: Lin, Guobin1 (AUTHOR), Hu, Xiaobin1 (AUTHOR) newhxb@whu.edu.cn
Source: Computers & Structures. Jun2025, Vol. 313, pN.PAG-N.PAG. 1p.
Subjects: Ground motion, Generative adversarial networks, Flexible structures, Earthquake engineering, Prior learning
Abstract: Near-fault (NF) ground motions usually have high-amplitude and long-period velocity pulses that might cause excessive responses in flexible structures. However, the number of recorded NF ground motions is very limited and hinders related research in earthquake engineering. In this paper, we develop a conditional generative adversarial network (CGAN) model, namely Ep2NgmGAN, to generate NF ground motions under given engineering parameters. Different from the traditional CGAN model, it inputs the label by introducing a label embedding module. In addition, a knowledge-enhanced module is adopted to enable the model to capture prior knowledge about NF ground motions. Using the strategy suggested in this study, the Ep2NgmGAN is trained and tested on the dataset constructed using the recorded NF ground motions and generated ones based on a mathematical method. Finally, numerical experiments and comparative investigations are carried out to comprehensively evaluate the performance of Ep2NgmGAN. The results indicate that the label embedding module is more suitable to deal with the continuous labels and the knowledge-enhanced module makes the model better learn the prior knowledge. In comparison to the representative mathematical methods, the Ep2NgmGAN has much higher efficiency and better or comparable accuracy, making it an appealing tool for NF ground motion synthesis. [ABSTRACT FROM AUTHOR]
Copyright of Computers & Structures is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 184998086
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PubType: Academic Journal
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  Data: Near-fault ground motion synthesis based on conditional generation adversarial network.
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  Data: <searchLink fieldCode="AR" term="%22Lin%2C+Guobin%22">Lin, Guobin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Xiaobin%22">Hu, Xiaobin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> newhxb@whu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Computers+%26+Structures%22">Computers & Structures</searchLink>. Jun2025, Vol. 313, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Ground+motion%22">Ground motion</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Flexible+structures%22">Flexible structures</searchLink><br /><searchLink fieldCode="DE" term="%22Earthquake+engineering%22">Earthquake engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Prior+learning%22">Prior learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Near-fault (NF) ground motions usually have high-amplitude and long-period velocity pulses that might cause excessive responses in flexible structures. However, the number of recorded NF ground motions is very limited and hinders related research in earthquake engineering. In this paper, we develop a conditional generative adversarial network (CGAN) model, namely Ep2NgmGAN, to generate NF ground motions under given engineering parameters. Different from the traditional CGAN model, it inputs the label by introducing a label embedding module. In addition, a knowledge-enhanced module is adopted to enable the model to capture prior knowledge about NF ground motions. Using the strategy suggested in this study, the Ep2NgmGAN is trained and tested on the dataset constructed using the recorded NF ground motions and generated ones based on a mathematical method. Finally, numerical experiments and comparative investigations are carried out to comprehensively evaluate the performance of Ep2NgmGAN. The results indicate that the label embedding module is more suitable to deal with the continuous labels and the knowledge-enhanced module makes the model better learn the prior knowledge. In comparison to the representative mathematical methods, the Ep2NgmGAN has much higher efficiency and better or comparable accuracy, making it an appealing tool for NF ground motion synthesis. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computers & Structures is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.compstruc.2025.107740
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Ground motion
        Type: general
      – SubjectFull: Generative adversarial networks
        Type: general
      – SubjectFull: Flexible structures
        Type: general
      – SubjectFull: Earthquake engineering
        Type: general
      – SubjectFull: Prior learning
        Type: general
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      – TitleFull: Near-fault ground motion synthesis based on conditional generation adversarial network.
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            NameFull: Lin, Guobin
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            NameFull: Hu, Xiaobin
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            – D: 01
              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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              Value: 313
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