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
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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
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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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