Application of Artificial Neural Networks in Identification of Affinity Hydration Model Parameters

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F12%3A00195507" target="_blank" >RIV/68407700:21110/12:00195507 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Application of Artificial Neural Networks in Identification of Affinity Hydration Model Parameters

Popis výsledku v původním jazyce

Recent decades have witnessed rapid development in numerical modelling of structures as well as materials and the complexity of models increases rapidly together with their computational demands. Despite the growing performance of modern computers and clusters, a suitable approximation of an exhaustive simulation has still many applications in engineering problems. For example, the field of parameters identification may represent a large domain for very efficient applications. The layered neural networks are still considered as very general tools for approximation and they became popular especially for their simple implementation. This contribution presents different strategies for application of neural networks in calibration of affinity hydration model and discusses their possible advantages and drawbacks.

Název v anglickém jazyce

Application of Artificial Neural Networks in Identification of Affinity Hydration Model Parameters

Popis výsledku anglicky

Recent decades have witnessed rapid development in numerical modelling of structures as well as materials and the complexity of models increases rapidly together with their computational demands. Despite the growing performance of modern computers and clusters, a suitable approximation of an exhaustive simulation has still many applications in engineering problems. For example, the field of parameters identification may represent a large domain for very efficient applications. The layered neural networks are still considered as very general tools for approximation and they became popular especially for their simple implementation. This contribution presents different strategies for application of neural networks in calibration of affinity hydration model and discusses their possible advantages and drawbacks.

Druh

D - Stať ve sborníku

CEP obor

JN - Stavebnictví

OECD FORD obor

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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

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Eighth International Conference on Engineering Computational Technology

ISBN

978-1-905088-55-3

ISSN

1759-3433

e-ISSN

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Počet stran výsledku

14

Strana od-do

1-14

Název nakladatele

Civil-Comp Press Ltd

Místo vydání

Stirling

Místo konání akce

Dubrovnik

Datum konání akce

4. 9. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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