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Reliability analysis of discrete-state performance functions via adaptive sequential sampling with detection of failure surfaces

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F22%3APU146359" target="_blank" >RIV/00216305:26110/22:PU146359 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1016/j.cma.2022.115606" target="_blank" >https://doi.org/10.1016/j.cma.2022.115606</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.cma.2022.115606" target="_blank" >10.1016/j.cma.2022.115606</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Reliability analysis of discrete-state performance functions via adaptive sequential sampling with detection of failure surfaces

  • Original language description

    The paper presents a new efficient and robust method for rare event probability estimation for computational models of an engineering product or a process returning categorical information only, for example, either success or failure. For such models, most of the methods designed for the estimation of failure probability, which use the numerical value of the outcome to compute gradients or to estimate the proximity to the failure surface, cannot be applied. Even if the performance function provides more than just binary output, the state of the system may be a non-smooth or even a discontinuous function defined in the domain of continuous input variables. This often happens because the mathematical model features non-smooth components or discontinuities (e.g., in the constitutive laws), bifurcations, or even domains in which no reasonable model response is obtained. In these cases, the classical gradient-based methods usually fail. We propose a simple yet efficient algorithm, which performs a sequential adaptive selection of points from the input domain of random variables to extend and refine a simple distance-based surrogate model. Two different tasks can be accomplished at any stage of sequential sampling: (i) estimation of the failure probability, and (ii) selection of the best possible candidate for the subsequent model evaluation if further improvement is necessary. The proposed criterion for selecting the next point for model evaluation maximizes the expected probability classified by using the candidate. Therefore, the perfect balance between global exploration and local exploitation is maintained automatically. If there are more rare events such as failure modes, the method can be generalized to estimate the probabilities of all these event types. Moreover, when the numerical value of model evaluation can be used to build a smooth surrogate, the algorithm can accommodate this information to increase the accuracy of the estimated probabilities. Lastly, we de

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    21100 - Other engineering and technologies

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

  • ISSN

    0045-7825

  • e-ISSN

    1879-2138

  • Volume of the periodical

    401

  • Issue of the periodical within the volume

    115606

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    46

  • Pages from-to

    „“-„“

  • UT code for WoS article

    000872540500004

  • EID of the result in the Scopus database

    2-s2.0-85139301092