Surrogate modelling of concrete girders using artificial neural network ensemble

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F20%3APU139337" target="_blank" >RIV/00216305:26110/20:PU139337 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1201/9780429343292-162" target="_blank" >http://dx.doi.org/10.1201/9780429343292-162</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1201/9780429343292-162" target="_blank" >10.1201/9780429343292-162</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Surrogate modelling of concrete girders using artificial neural network ensemble

Popis výsledku v původním jazyce

Surrogate model as approximation model is widely applied in engineering application, perform-ing sufficient amount of simulations for a fully probabilistic design of computationally demanding tasks. There are several types of new surrogate modelling techniques in last decades. Herein, artificial neural network en-semble (ANNE) is employed for surrogate modelling, which is very efficient method as will be shown on analytical example. Moreover, a comparison of traditional single artificial neural network approach and ANNE will be presented as well. Main part of the paper will be focused on application of ANNE for stochastic analysis of concrete girders represented by mathematical model in form of nonlinear finite element model. Therefore, in the last part of the paper, results obtained by ANNE will be presented and discussed.

Název v anglickém jazyce

Surrogate modelling of concrete girders using artificial neural network ensemble

Popis výsledku anglicky

Surrogate model as approximation model is widely applied in engineering application, perform-ing sufficient amount of simulations for a fully probabilistic design of computationally demanding tasks. There are several types of new surrogate modelling techniques in last decades. Herein, artificial neural network en-semble (ANNE) is employed for surrogate modelling, which is very efficient method as will be shown on analytical example. Moreover, a comparison of traditional single artificial neural network approach and ANNE will be presented as well. Main part of the paper will be focused on application of ANNE for stochastic analysis of concrete girders represented by mathematical model in form of nonlinear finite element model. Therefore, in the last part of the paper, results obtained by ANNE will be presented and discussed.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20101 - Civil engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Life-Cycle Civil Engineering: Innovation, Theory and Practice. Proceedings of the 7th International Symposium on Life-Cycle Civil Engineering (IALCCE 2020), October 27-30, 2020, Shanghai, China

ISBN

9780429343292

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Neuveden

Místo vydání

Shanghai, China

Místo konání akce

Shanghai

Datum konání akce

27. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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