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    Efficient and Comprehensive Time-Dependent Reliability Analysis of Complex Structures by a Parameter State Model

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2021:;Volume ( 007 ):;issue: 002::page 04021020-1
    Author:
    Florian Blandfort
    ,
    Christian Glock
    ,
    Jörn Sass
    ,
    Stefanie Schwaar
    ,
    Rabea Sefrin
    DOI: 10.1061/AJRUA6.0001130
    Publisher: ASCE
    Abstract: A major challenge in a time-dependent reliability analysis is to find a good balance between computational effort, accuracy, and comprehension of the analysis. In this contribution, computational efforts are reduced, and comprehension is increased by changing the perspective from a classical time-dependent probabilistic model to an equivalent parameter state model. The model maps all time-dependent information to one stochastic variable, allowing one to analyze the original problem by analyzing changes in the distribution of this single variable. This clear-cut structure allows for analyzing and visualizing dependencies in detail as well as a novel perspective on reliability estimation, even in complex settings. Furthermore, it just needs computational effort as low as that for a static reliability estimation in many cases. Our approach utilizes, but is not restricted to, Subset Simulation and is therefore especially designed for complex engineering structures of high dimension. To illustrate the model and its computational efficiency, an illustrative example in the time-dependent reliability analysis of concrete structures is presented, and it is shown how to analyze complex networks potentially.
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      Efficient and Comprehensive Time-Dependent Reliability Analysis of Complex Structures by a Parameter State Model

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    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

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    contributor authorFlorian Blandfort
    contributor authorChristian Glock
    contributor authorJörn Sass
    contributor authorStefanie Schwaar
    contributor authorRabea Sefrin
    date accessioned2022-01-31T23:59:12Z
    date available2022-01-31T23:59:12Z
    date issued6/1/2021
    identifier otherAJRUA6.0001130.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4270695
    description abstractA major challenge in a time-dependent reliability analysis is to find a good balance between computational effort, accuracy, and comprehension of the analysis. In this contribution, computational efforts are reduced, and comprehension is increased by changing the perspective from a classical time-dependent probabilistic model to an equivalent parameter state model. The model maps all time-dependent information to one stochastic variable, allowing one to analyze the original problem by analyzing changes in the distribution of this single variable. This clear-cut structure allows for analyzing and visualizing dependencies in detail as well as a novel perspective on reliability estimation, even in complex settings. Furthermore, it just needs computational effort as low as that for a static reliability estimation in many cases. Our approach utilizes, but is not restricted to, Subset Simulation and is therefore especially designed for complex engineering structures of high dimension. To illustrate the model and its computational efficiency, an illustrative example in the time-dependent reliability analysis of concrete structures is presented, and it is shown how to analyze complex networks potentially.
    publisherASCE
    titleEfficient and Comprehensive Time-Dependent Reliability Analysis of Complex Structures by a Parameter State Model
    typeJournal Paper
    journal volume7
    journal issue2
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
    identifier doi10.1061/AJRUA6.0001130
    journal fristpage04021020-1
    journal lastpage04021020-12
    page12
    treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2021:;Volume ( 007 ):;issue: 002
    contenttypeFulltext
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