Application of soft computing techniques for reliability calculation of time demanding problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F15%3APU117430" target="_blank" >RIV/00216305:26110/15:PU117430 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Application of soft computing techniques for reliability calculation of time demanding problems

Popis výsledku v původním jazyce

The reliability analysis of complex structural systems requires utilization of approximation methods for calculation of reliability measures with the view of reduction of computational efforts to an acceptable level. In the paper, an artificial neural network based response surface method in combination with the small-sample simulation technique is presented. An artificial neural network is used as a surrogate model for approximation of original limit state function. Efficiency is emphasized by utilization of the stratified simulation for the selection of neural network training set elements. Response surface obtained is independent of the type of distribution or correlations among the basic variables which enables sensitivity studies with respect to these parameters without much computational effort. Subsequently, the ANN surrogate model is utilized in conjunction with Monte Carlo simulation method to obtain desired reliability measures. The proposed method is tested using nonlinear limit state function taken from the literature as well as employed for reliability assessment of concrete bridge.

Název v anglickém jazyce

Application of soft computing techniques for reliability calculation of time demanding problems

Popis výsledku anglicky

The reliability analysis of complex structural systems requires utilization of approximation methods for calculation of reliability measures with the view of reduction of computational efforts to an acceptable level. In the paper, an artificial neural network based response surface method in combination with the small-sample simulation technique is presented. An artificial neural network is used as a surrogate model for approximation of original limit state function. Efficiency is emphasized by utilization of the stratified simulation for the selection of neural network training set elements. Response surface obtained is independent of the type of distribution or correlations among the basic variables which enables sensitivity studies with respect to these parameters without much computational effort. Subsequently, the ANN surrogate model is utilized in conjunction with Monte Carlo simulation method to obtain desired reliability measures. The proposed method is tested using nonlinear limit state function taken from the literature as well as employed for reliability assessment of concrete bridge.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

<a href="/cs/project/GA15-07730S" target="_blank" >GA15-07730S: Přímá a inverzní spolehlivostní optimalizace s ohledem na nejistoty (FIRBO)</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Safety and Reliability of Complex Engineered Systems: Proceedings of the 25th European Safety and Reliability Conference, ESREL 2015, Zürich, Switzerland, 7–10 September 2015

ISBN

978-1-138-02879-1

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

4151-4159

Název nakladatele

L. Podofillini et al.

Místo vydání

Curych, Švýcarsko

Místo konání akce

Zürich

Datum konání akce

3. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—