Surrogate modelling of concrete girders using artificial neural network ensemble

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Surrogate modelling of concrete girders using artificial neural network ensemble
Original language description
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.
Czech name
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Czech description
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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)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
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
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e-ISSN
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Number of pages
5
Pages from-to
1-5
Publisher name
Neuveden
Place of publication
Shanghai, China
Event location
Shanghai
Event date
Oct 27, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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