Neural network based damage detection of dynamically loaded structures
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F09%3APU85960" target="_blank" >RIV/00216305:26110/09:PU85960 - 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
Neural network based damage detection of dynamically loaded structures
Original language description
The aim of the paper is to describe a methodology of damage detection which is based on artificial neural networks in combination with stochastic analysis. The damage is defined as a stiffness reduction (bending or torsion) in certain part of a structure. The key stone of the method is feed-forward multilayer network. It is impossible to obtain appropriate training set for real structure in usage, therefore stochastic analysis using numerical model is carried out to get training set virtually. Due to possible time demanding nonlinear calculations the effective simulation Latin Hypercube Sampling is used here. The important part of identification process is proper selection of input information. In case of dynamically loaded structures their modal properties seem to be proper input information as those are not dependent on actual loading (traffic, wind, temperature). The methodology verification was carried out using laboratory beam.
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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
11th International Conference on Engineering Applications of Neural Networks (EANN 2009)
ISBN
978-3-642-03968-3
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
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Publisher name
Neuveden
Place of publication
London, UK
Event location
London
Event date
Aug 27, 2009
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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