Utilization of Artificial Neural Network Based Response Surface Method for Reliability Analysis of Structures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F15%3APU117442" target="_blank" >RIV/00216305:26110/15:PU117442 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Utilization of Artificial Neural Network Based Response Surface Method for Reliability Analysis of Structures

Popis výsledku v původním jazyce

The key step of the reliability and lifetime assessment of structures is the determination of reliability level, described by failure probability or reliability index. Some of the simulation or approximation techniques can be used for this purpose. In case of large structures analyzed using the nonlinear finite element method, it is necessary to develop more efficient procedures, reducing the number of evaluations of original limit state function to a minimum. Here, artificial neural network based response surface method in combination with small-sample simulation technique Latin Hypercube Sampling is utilized for the approximation of a limit state function. Thanks to ability of artificial neural network to generalize it is efficient to fit limit statefunction with a sufficiently small number of simulations.

Název v anglickém jazyce

Utilization of Artificial Neural Network Based Response Surface Method for Reliability Analysis of Structures

Popis výsledku anglicky

The key step of the reliability and lifetime assessment of structures is the determination of reliability level, described by failure probability or reliability index. Some of the simulation or approximation techniques can be used for this purpose. In case of large structures analyzed using the nonlinear finite element method, it is necessary to develop more efficient procedures, reducing the number of evaluations of original limit state function to a minimum. Here, artificial neural network based response surface method in combination with small-sample simulation technique Latin Hypercube Sampling is utilized for the approximation of a limit state function. Thanks to ability of artificial neural network to generalize it is efficient to fit limit statefunction with a sufficiently small number of simulations.

Druh

O - Ostatní výsledky

CEP obor

JM - Inženýrské stavitelství

OECD FORD obor

—

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