Small-sample artificial neural network based response surface method for reliability analysis of concrete bridges

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F15%3APU112435" target="_blank" >RIV/00216305:26110/15:PU112435 - 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

Small-sample artificial neural network based response surface method for reliability analysis of concrete bridges

Popis výsledku v původním jazyce

In the paper, an artificial neural network based response surface method (ANN-RSM) in combination with a small-sample simulation technique is proposed. ANN as powerful parallel computational system is used for approximation of limit state function (LSF). Thanks to its ability to generalize it is efficient to fit LSF even with small number of simulations compared to polynomial RSM. Efficiency is emphasized by utilization of stratified simulation for selection of ANN training set elements. Proposed method is tested using simple limit state function taken from literature as well as employed for reliability and load-bearing capacity assessment of concrete bridge within the framework of fully probabilistic analysis. Results are compared with those obtained by other reliability methods.

Název v anglickém jazyce

Small-sample artificial neural network based response surface method for reliability analysis of concrete bridges

Popis výsledku anglicky

In the paper, an artificial neural network based response surface method (ANN-RSM) in combination with a small-sample simulation technique is proposed. ANN as powerful parallel computational system is used for approximation of limit state function (LSF). Thanks to its ability to generalize it is efficient to fit LSF even with small number of simulations compared to polynomial RSM. Efficiency is emphasized by utilization of stratified simulation for selection of ANN training set elements. Proposed method is tested using simple limit state function taken from literature as well as employed for reliability and load-bearing capacity assessment of concrete bridge within the framework of fully probabilistic analysis. Results are compared with those obtained by other reliability methods.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/LH14334" target="_blank" >LH14334: Účinné přístupy neurocomputing pro analýzu a posouzení konstrukcí</a><br>

Návaznosti

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

Proceedings of the Fourth International Symposium on Life-Cycle Civil Engineering (IALCCE 2014) – Life-Cycle of Structural Systems: Design, Assessment, Maintenance and Management

ISBN

978-1-138-00120-6

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

1903-1909

Název nakladatele

Taylor & Francis Group

Místo vydání

London, UK

Místo konání akce

Tokyo

Datum konání akce

16. 11. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000380508800254