Academic Journal

Backward Recursive Method for Structural Reliability Calculation Based on the Universal Generating Function.

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Τίτλος: Backward Recursive Method for Structural Reliability Calculation Based on the Universal Generating Function.
Συγγραφείς: Zhou, Jinyu1 (AUTHOR) yuhangyuan888@sina.com, Xie, Liyang2 (AUTHOR) lyxie@mail.neu.edu.cn, Han, Wenqin3 (AUTHOR) hwq@jsut.edu.cn
Πηγή: IEEE Transactions on Reliability. Dec2021, Vol. 70 Issue 4, p1332-1341. 10p.
Θεματικοί όροι: *Random variables, Structural reliability, Generating functions, Characteristic functions
Περίληψη: For structural performance functions with characteristics of multivariable, non-normality, small failure probability and nonlinearity, it is usually difficult to pursue satisfactory accuracy by means of traditional probabilistic methods such as first-order reliability method and second-order reliability method. According to the idea of adaptive subdivision, a backward recursive method based on the universal generating function is suggested for structural reliability calculation. In the sensitive region, the variable space is subdivided partially, the discrete interval length is reduced, and the random variables are discretized nonuniformly and adaptively by the backward recursion operation. Compared with the current method of forward recursion operator and the corresponding similar items merging operation, the backward recursion operator can not only avoid low accuracy owing to subdividing the state combinations in the sensitive region, but avert the high cost of calculation owing to merging state combinations in the nonsensitive region of the variable space as well. Three examples are presented to demonstrate the rationality of the backward recursive algorithm. The results indicate that the accuracy of the proposed method is significantly higher than that of traditional methods, and the computational efficiency of the proposed method meets the engineering requirement. The research work provides a new path for reliability calculation with consideration of complex performance functions. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Reliability is the property of IEEE 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: Backward Recursive Method for Structural Reliability Calculation Based on the Universal Generating Function.
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Reliability%22">IEEE Transactions on Reliability</searchLink>. Dec2021, Vol. 70 Issue 4, p1332-1341. 10p.
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  Data: *<searchLink fieldCode="DE" term="%22Random+variables%22">Random variables</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+reliability%22">Structural reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Generating+functions%22">Generating functions</searchLink><br /><searchLink fieldCode="DE" term="%22Characteristic+functions%22">Characteristic functions</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: For structural performance functions with characteristics of multivariable, non-normality, small failure probability and nonlinearity, it is usually difficult to pursue satisfactory accuracy by means of traditional probabilistic methods such as first-order reliability method and second-order reliability method. According to the idea of adaptive subdivision, a backward recursive method based on the universal generating function is suggested for structural reliability calculation. In the sensitive region, the variable space is subdivided partially, the discrete interval length is reduced, and the random variables are discretized nonuniformly and adaptively by the backward recursion operation. Compared with the current method of forward recursion operator and the corresponding similar items merging operation, the backward recursion operator can not only avoid low accuracy owing to subdividing the state combinations in the sensitive region, but avert the high cost of calculation owing to merging state combinations in the nonsensitive region of the variable space as well. Three examples are presented to demonstrate the rationality of the backward recursive algorithm. The results indicate that the accuracy of the proposed method is significantly higher than that of traditional methods, and the computational efficiency of the proposed method meets the engineering requirement. The research work provides a new path for reliability calculation with consideration of complex performance functions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Reliability is the property of IEEE 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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      – Type: doi
        Value: 10.1109/TR.2020.3047415
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 1332
    Subjects:
      – SubjectFull: Random variables
        Type: general
      – SubjectFull: Structural reliability
        Type: general
      – SubjectFull: Generating functions
        Type: general
      – SubjectFull: Characteristic functions
        Type: general
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      – TitleFull: Backward Recursive Method for Structural Reliability Calculation Based on the Universal Generating Function.
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              M: 12
              Text: Dec2021
              Type: published
              Y: 2021
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