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Utilization of Artificial Neural Network Based Response Surface Method for Reliability Analysis of Structures

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F15%3APU117442" target="_blank" >RIV/00216305:26110/15:PU117442 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Utilization of Artificial Neural Network Based Response Surface Method for Reliability Analysis of Structures

  • Original language description

    The key step of the reliability and lifetime assessment of structures is the determination of reliability level, described by failure probability or reliability index. Some of the simulation or approximation techniques can be used for this purpose. In case of large structures analyzed using the nonlinear finite element method, it is necessary to develop more efficient procedures, reducing the number of evaluations of original limit state function to a minimum. Here, artificial neural network based response surface method in combination with small-sample simulation technique Latin Hypercube Sampling is utilized for the approximation of a limit state function. Thanks to ability of artificial neural network to generalize it is efficient to fit limit statefunction with a sufficiently small number of simulations.

  • Czech name

  • Czech description

Classification

  • Type

    O - Miscellaneous

  • CEP classification

    JM - Structural engineering

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GA15-07730S" target="_blank" >GA15-07730S: Forward and inverse reliability-based optimization under uncertainties (FIRBO)</a><br>

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2015

  • Confidentiality

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