Bayesian parameter identification of lattice discrete particle model for concrete

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F18%3A00322811" target="_blank" >RIV/68407700:21110/18:00322811 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.21495/91-8-325" target="_blank" >http://dx.doi.org/10.21495/91-8-325</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21495/91-8-325" target="_blank" >10.21495/91-8-325</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian parameter identification of lattice discrete particle model for concrete

Popis výsledku v původním jazyce

The investigated lattice discrete particle model allows to simulate the failure behaviour of concrete. The main characteristic of this model is its response dependence on randomly generated geometrical features of material internal structure according to the basic concrete properties and the size distribution of the aggregates. This makes the model stochastic and the corresponding calibration difficult. On top of this, the model simulation is computationally demanding which limits usage of the full numerical model in an identification procedure. To overcome these obstacles, the polynomial chaos based surrogate model is employed. This approximation eradicates the original model stochasticity, but the corresponding uncertainty can be estimated from the approximation error. The quantified uncertainty is then involved in the Bayesian parameter inference based on Markov chain Monte Carlo sampling with use of cheap surrogate evaluations. The proposed identification procedure is verified on a basis of synthetic experimental data set from notched three-point-bending tests and cube compression tests.

Název v anglickém jazyce

Bayesian parameter identification of lattice discrete particle model for concrete

Popis výsledku anglicky

The investigated lattice discrete particle model allows to simulate the failure behaviour of concrete. The main characteristic of this model is its response dependence on randomly generated geometrical features of material internal structure according to the basic concrete properties and the size distribution of the aggregates. This makes the model stochastic and the corresponding calibration difficult. On top of this, the model simulation is computationally demanding which limits usage of the full numerical model in an identification procedure. To overcome these obstacles, the polynomial chaos based surrogate model is employed. This approximation eradicates the original model stochasticity, but the corresponding uncertainty can be estimated from the approximation error. The quantified uncertainty is then involved in the Bayesian parameter inference based on Markov chain Monte Carlo sampling with use of cheap surrogate evaluations. The proposed identification procedure is verified on a basis of synthetic experimental data set from notched three-point-bending tests and cube compression tests.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/GJ16-11473Y" target="_blank" >GJ16-11473Y: Identifikace aleatorické nejistoty v parametrech heterogenních materiálů</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Engineering Mechanics 2018: Book of Full Texts

ISBN

978-80-86246-88-8

ISSN

1805-8248

e-ISSN

—

Počet stran výsledku

4

Strana od-do

325-328

Název nakladatele

Ústav teoretické a aplikované mechaniky AV ČR, v. v. i.

Místo vydání

Praha

Místo konání akce

Svratka

Datum konání akce

14. 5. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—