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A neural network copula function approach for solving joint basic probability assignment in structural reliability analysis.

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Title: A neural network copula function approach for solving joint basic probability assignment in structural reliability analysis.
Authors: Yang, Rui‐Shi1,2 (AUTHOR), Sun, Li‐Jun1,2 (AUTHOR), Li, Hai‐Bin1,2 (AUTHOR) lhbnm2003@126.com, Yang, Yong1,2 (AUTHOR)
Source: Quality & Reliability Engineering International. Oct2024, Vol. 40 Issue 6, p3096-3119. 24p.
Subjects: Copula functions, Structural reliability, Epistemic uncertainty, Engineering reliability theory, Data distribution
Abstract: Applying evidence theory to structural reliability analysis under epistemic uncertainty, it is necessary to consider the correlation of evidence variables. Among them, solving the joint basic probability assignment (BPA) of the evidence variables is a crucial link. In this study, a solution method of joint BPA based on neural network copula function is proposed. This method is to automatically construct copula function through neural network, which avoids the process of selecting the optimal copula function. Firstly, the neural network copula function is constructed based on the sample set of evidence variables. Then, the expression for solving the joint BPA using the neural network copula function is derived through vectors. Furthermore, the expression is used to map the marginal BPA of evidence variables to joint BPA, thus realizing the solution of joint BPA. Finally, the effectiveness of this method is verified by three examples. The results show that the neural network copula function describes the data distribution better than the optimal copula function selected by the traditional method. In addition, there is actually an error in solving the reliability intervals using the traditional optimal copula function method, whereas the results of this paper's neural network copula function method are more accurate and better for decision making. [ABSTRACT FROM AUTHOR]
Copyright of Quality & Reliability Engineering International is the property of Wiley-Blackwell 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: A neural network copula function approach for solving joint basic probability assignment in structural reliability analysis.
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Rui‐Shi%22">Yang, Rui‐Shi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Li‐Jun%22">Sun, Li‐Jun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Hai‐Bin%22">Li, Hai‐Bin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> lhbnm2003@126.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Yong%22">Yang, Yong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Quality+%26+Reliability+Engineering+International%22">Quality & Reliability Engineering International</searchLink>. Oct2024, Vol. 40 Issue 6, p3096-3119. 24p.
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  Data: <searchLink fieldCode="DE" term="%22Copula+functions%22">Copula functions</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+reliability%22">Structural reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Epistemic+uncertainty%22">Epistemic uncertainty</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+reliability+theory%22">Engineering reliability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Data+distribution%22">Data distribution</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Applying evidence theory to structural reliability analysis under epistemic uncertainty, it is necessary to consider the correlation of evidence variables. Among them, solving the joint basic probability assignment (BPA) of the evidence variables is a crucial link. In this study, a solution method of joint BPA based on neural network copula function is proposed. This method is to automatically construct copula function through neural network, which avoids the process of selecting the optimal copula function. Firstly, the neural network copula function is constructed based on the sample set of evidence variables. Then, the expression for solving the joint BPA using the neural network copula function is derived through vectors. Furthermore, the expression is used to map the marginal BPA of evidence variables to joint BPA, thus realizing the solution of joint BPA. Finally, the effectiveness of this method is verified by three examples. The results show that the neural network copula function describes the data distribution better than the optimal copula function selected by the traditional method. In addition, there is actually an error in solving the reliability intervals using the traditional optimal copula function method, whereas the results of this paper's neural network copula function method are more accurate and better for decision making. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Quality & Reliability Engineering International is the property of Wiley-Blackwell 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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    Identifiers:
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        Value: 10.1002/qre.3568
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      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 3096
    Subjects:
      – SubjectFull: Copula functions
        Type: general
      – SubjectFull: Structural reliability
        Type: general
      – SubjectFull: Epistemic uncertainty
        Type: general
      – SubjectFull: Engineering reliability theory
        Type: general
      – SubjectFull: Data distribution
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      – TitleFull: A neural network copula function approach for solving joint basic probability assignment in structural reliability analysis.
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            NameFull: Yang, Rui‐Shi
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            NameFull: Sun, Li‐Jun
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            NameFull: Li, Hai‐Bin
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            – D: 01
              M: 10
              Text: Oct2024
              Type: published
              Y: 2024
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