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    Structural Reliability Analysis Using Generalized Distribution Reconstruction Method with Novel Improvements

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2024:;Volume ( 010 ):;issue: 002::page 04024028-1
    Author:
    Zhiqiang Wan
    ,
    Yuan Gao
    ,
    Weifeng Tao
    DOI: 10.1061/AJRUA6.RUENG-1298
    Publisher: American Society of Civil Engineers
    Abstract: The probability density function (PDF) of structural responses is one of the significant fundamentals for structural reliability analysis. Nonetheless, it is still challenging to compute the PDF, especially when the distribution tail is concerned for the structural reliability. Because an arbitrary PDF is the exact inverse Fourier transform of its corresponding characteristic function (CF), working with the CF provides an alternative to obtain the PDF. Recently, based on the inverse Fourier transform of CF, which can be numerically calculated with the complex fractional moments, a generalized distribution reconstruction (GDR) method was proposed in the literature. This paper aims to provide new improvements to the GDR method, including (1) giving a theoretical explanation to demonstrate why the GDR method could reconstruct the PDF in an accurate way; (2) proposing two new expressions to approximate the CF with fewer undetermined parameters, which strictly satisfy the mathematical properties of CF; and (3) deriving an analytical PDF such that possible errors from the numerical inverse Fourier transform can be avoided. Analytical distributions and practical applications are studied to illustrate the efficiency and accuracy of the proposed approach. Some open issues to be further investigated are outlined as well.
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      Structural Reliability Analysis Using Generalized Distribution Reconstruction Method with Novel Improvements

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4298645
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    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

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    contributor authorZhiqiang Wan
    contributor authorYuan Gao
    contributor authorWeifeng Tao
    date accessioned2024-12-24T10:17:35Z
    date available2024-12-24T10:17:35Z
    date copyright6/1/2024 12:00:00 AM
    date issued2024
    identifier otherAJRUA6.RUENG-1298.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4298645
    description abstractThe probability density function (PDF) of structural responses is one of the significant fundamentals for structural reliability analysis. Nonetheless, it is still challenging to compute the PDF, especially when the distribution tail is concerned for the structural reliability. Because an arbitrary PDF is the exact inverse Fourier transform of its corresponding characteristic function (CF), working with the CF provides an alternative to obtain the PDF. Recently, based on the inverse Fourier transform of CF, which can be numerically calculated with the complex fractional moments, a generalized distribution reconstruction (GDR) method was proposed in the literature. This paper aims to provide new improvements to the GDR method, including (1) giving a theoretical explanation to demonstrate why the GDR method could reconstruct the PDF in an accurate way; (2) proposing two new expressions to approximate the CF with fewer undetermined parameters, which strictly satisfy the mathematical properties of CF; and (3) deriving an analytical PDF such that possible errors from the numerical inverse Fourier transform can be avoided. Analytical distributions and practical applications are studied to illustrate the efficiency and accuracy of the proposed approach. Some open issues to be further investigated are outlined as well.
    publisherAmerican Society of Civil Engineers
    titleStructural Reliability Analysis Using Generalized Distribution Reconstruction Method with Novel Improvements
    typeJournal Article
    journal volume10
    journal issue2
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
    identifier doi10.1061/AJRUA6.RUENG-1298
    journal fristpage04024028-1
    journal lastpage04024028-18
    page18
    treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2024:;Volume ( 010 ):;issue: 002
    contenttypeFulltext
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