Small-sample artificial neural network based response surface method for reliability analysis of concrete bridges
The result's identifiers
Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Small-sample artificial neural network based response surface method for reliability analysis of concrete bridges
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20101 - Civil engineering
Result continuities
Project
<a href="/en/project/LH14334" target="_blank" >LH14334: Efficient neurocomputing approaches for structural analysis and assessment</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
7
Pages from-to
1903-1909
Publisher name
Taylor & Francis Group
Place of publication
London, UK
Event location
Tokyo
Event date
Nov 16, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000380508800254