Statistical material parameters identification based on artificial neural networks for stochastic computations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F17%3APU124201" target="_blank" >RIV/00216305:26110/17:PU124201 - isvavai.cz</a>

Výsledek na webu

<a href="http://aip.scitation.org/doi/abs/10.1063/1.4989942" target="_blank" >http://aip.scitation.org/doi/abs/10.1063/1.4989942</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/1.4989942" target="_blank" >10.1063/1.4989942</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Statistical material parameters identification based on artificial neural networks for stochastic computations

Popis výsledku v původním jazyce

A general methodology to obtain statistical material model parameters is presented. The procedure is based on the coupling of a stochastic simulation and an artificial neural network. The identification parameters play the role of basic random variables with a scatter reflecting the physical range of possible values. The efficient small-sample simulation method Latin Hypercube Sampling is used for the stochastic preparation of the training set utilized in training the neural network. Once the network has been trained, it represents an approximation consequently utilized in a following way: To provide the best possible set of model parameters for the given experimental data. The paper focuses the attention on the statistical inverse analysis of material model parameters where statistical moments (usually means and standard deviations) of input parameters have to be identified based on experimental data. A hierarchical statistical parameters database within the framework of reliability software is presented. The efficiency of the approach is verified using numerical example of fracture-mechanical parameters determination of fiber reinforced and plain concretes.

Název v anglickém jazyce

Statistical material parameters identification based on artificial neural networks for stochastic computations

Popis výsledku anglicky

A general methodology to obtain statistical material model parameters is presented. The procedure is based on the coupling of a stochastic simulation and an artificial neural network. The identification parameters play the role of basic random variables with a scatter reflecting the physical range of possible values. The efficient small-sample simulation method Latin Hypercube Sampling is used for the stochastic preparation of the training set utilized in training the neural network. Once the network has been trained, it represents an approximation consequently utilized in a following way: To provide the best possible set of model parameters for the given experimental data. The paper focuses the attention on the statistical inverse analysis of material model parameters where statistical moments (usually means and standard deviations) of input parameters have to be identified based on experimental data. A hierarchical statistical parameters database within the framework of reliability software is presented. The efficiency of the approach is verified using numerical example of fracture-mechanical parameters determination of fiber reinforced and plain concretes.

Druh

D - Stať ve sborníku

CEP obor

—

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

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

The 2nd International Conference on Smart Materials Technologies

ISBN

978-0-7354-1532-4

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

„020005-1“-„020005-7“

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

St.Petersburg

Datum konání akce

19. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000410618900005