Reliability Analysis of Post-Tensioned Bridge Using Artificial Neural Network-Based Surrogate Model
The result's identifiers
Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Reliability Analysis of Post-Tensioned Bridge Using Artificial Neural Network-Based Surrogate Model
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JM - Structural engineering
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA15-07730S" target="_blank" >GA15-07730S: Forward and inverse reliability-based optimization under uncertainties (FIRBO)</a><br>
Continuities
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
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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Number of pages
10
Pages from-to
35-44
Publisher name
L. Iliadis and Ch. Jayne
Place of publication
Rhodos, Řecko
Event location
Rhodes, Greece
Event date
Sep 25, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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