Reliability Analysis of Post-Tensioned Bridge Using Artificial Neural Network-Based Surrogate Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F15%3APU117437" target="_blank" >RIV/00216305:26110/15:PU117437 - 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

Reliability Analysis of Post-Tensioned Bridge Using Artificial Neural Network-Based Surrogate Model

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. The aim is to replace the original limit state function by an approximation, the so-called response surface, whose function values can be computed more easily. In the paper, an artificial neural network based response surface method in the combination with the small-sample simulation technique is introduced. 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. The proposed method is employed for reliability assessment of post-tensioned composite bridge. Response surface obtained is independent of the type of distribution or correlations among the basic variables.

Název v anglickém jazyce

Reliability Analysis of Post-Tensioned Bridge Using Artificial Neural Network-Based Surrogate Model

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. The aim is to replace the original limit state function by an approximation, the so-called response surface, whose function values can be computed more easily. In the paper, an artificial neural network based response surface method in the combination with the small-sample simulation technique is introduced. 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. The proposed method is employed for reliability assessment of post-tensioned composite bridge. Response surface obtained is independent of the type of distribution or correlations among the basic variables.

Druh

D - Stať ve sborníku

CEP obor

JM - Inženýrské stavitelství

OECD FORD obor

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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

Engineering Applications of Neural Networks, Proceedings of the 16th International Conference, EANN 2015, Rhodes, Greece, September 25?28, 2015

ISBN

978-3-319-23983-5

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

35-44

Název nakladatele

L. Iliadis and Ch. Jayne

Místo vydání

Rhodos, Řecko

Místo konání akce

Rhodes, Greece

Datum konání akce

25. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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