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Random vibration load identification of a cylindrical structure using data Deep Recurrent Neural Network.

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Title: Random vibration load identification of a cylindrical structure using data Deep Recurrent Neural Network.
Authors: Ouyang, Qinshan1 (AUTHOR), Dong, Longlei1 (AUTHOR) dongll@xjtu.edu.cn, Zhou, Jiaming2 (AUTHOR), Liu, Jian1 (AUTHOR)
Source: International Journal of Applied Electromagnetics & Mechanics. Jul2025, Vol. 78 Issue 1-3, p35-39. 5p.
Subjects: Random vibration, Recurrent neural networks, Machine learning, Strains & stresses (Mechanics), Generalization, Cylinder (Shapes), Long short-term memory
Abstract: A novel data-driven Deep Recurrent Neural Network (data-DRNN) method is proposed for the time-domain load identification of a cylindrical structure subjected to random vibration excitation by an electromagnetic shaker. The data-DRNN model comprises of two Long Short-Term Memory (LSTM) layers and one Bidirectional LSTM (BLSTM) layer, and is trained using a large dataset of loads and corresponding responses data. The effectiveness and accuracy of the proposed method have been validated by analyzing the trapezoidal spectrum excited response data under various temperature conditions. Furthermore, the model's generalization capability has been evaluated by examining different testing target spectrums. The results indicate that data-DRNN has excellent accuracy and generalization ability, making it a promising choice for load identification of random vibration. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Applied Electromagnetics & Mechanics is the property of Sage Publications Inc. 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.)
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  Data: Random vibration load identification of a cylindrical structure using data Deep Recurrent Neural Network.
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  Data: <searchLink fieldCode="AR" term="%22Ouyang%2C+Qinshan%22">Ouyang, Qinshan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Longlei%22">Dong, Longlei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dongll@xjtu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Jiaming%22">Zhou, Jiaming</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Jian%22">Liu, Jian</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Applied+Electromagnetics+%26+Mechanics%22">International Journal of Applied Electromagnetics & Mechanics</searchLink>. Jul2025, Vol. 78 Issue 1-3, p35-39. 5p.
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  Data: <searchLink fieldCode="DE" term="%22Random+vibration%22">Random vibration</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Strains+%26+stresses+%28Mechanics%29%22">Strains & stresses (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Generalization%22">Generalization</searchLink><br /><searchLink fieldCode="DE" term="%22Cylinder+%28Shapes%29%22">Cylinder (Shapes)</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: A novel data-driven Deep Recurrent Neural Network (data-DRNN) method is proposed for the time-domain load identification of a cylindrical structure subjected to random vibration excitation by an electromagnetic shaker. The data-DRNN model comprises of two Long Short-Term Memory (LSTM) layers and one Bidirectional LSTM (BLSTM) layer, and is trained using a large dataset of loads and corresponding responses data. The effectiveness and accuracy of the proposed method have been validated by analyzing the trapezoidal spectrum excited response data under various temperature conditions. Furthermore, the model's generalization capability has been evaluated by examining different testing target spectrums. The results indicate that data-DRNN has excellent accuracy and generalization ability, making it a promising choice for load identification of random vibration. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Applied Electromagnetics & Mechanics is the property of Sage Publications Inc. 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:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/13835416251329172
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 5
        StartPage: 35
    Subjects:
      – SubjectFull: Random vibration
        Type: general
      – SubjectFull: Recurrent neural networks
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Strains & stresses (Mechanics)
        Type: general
      – SubjectFull: Generalization
        Type: general
      – SubjectFull: Cylinder (Shapes)
        Type: general
      – SubjectFull: Long short-term memory
        Type: general
    Titles:
      – TitleFull: Random vibration load identification of a cylindrical structure using data Deep Recurrent Neural Network.
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            NameFull: Ouyang, Qinshan
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            NameFull: Dong, Longlei
      – PersonEntity:
          Name:
            NameFull: Zhou, Jiaming
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          Name:
            NameFull: Liu, Jian
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          Dates:
            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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              Value: 78
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            – TitleFull: International Journal of Applied Electromagnetics & Mechanics
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