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Sensitivity analysis of prestressed concrete girders based on artificial neural network surrogate model.

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F18%3APU131766" target="_blank" >RIV/00216305:26110/18:PU131766 - isvavai.cz</a>

  • Result on the web

    <a href="https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fbest.201800059&file=best201800059-sup-0001-suppinfo.pdf" target="_blank" >https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fbest.201800059&file=best201800059-sup-0001-suppinfo.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Sensitivity analysis of prestressed concrete girders based on artificial neural network surrogate model.

  • Original language description

    The paper describes a neural network ensemble-based parameter sensitivity analysis, which is compared with selected sensitivity analysis techniques usually utilized in stochastic structural modeling. The accuracy, stability and efficiency of the mentioned sensitivity analysis techniques are compared on example of prestressed concrete girder.

  • Czech name

  • Czech description

Classification

  • Type

    O - Miscellaneous

  • CEP classification

  • OECD FORD branch

    20101 - Civil engineering

Result continuities

  • Project

    <a href="/en/project/GA17-02862S" target="_blank" >GA17-02862S: Probabilistic modelling and optimization of shear strength of concrete beams (PROMOSS)</a><br>

  • Continuities

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

Others

  • Publication year

    2018

  • Confidentiality

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