Institutional Repository
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Nondestructive elastostatic identification of unilateral cracks through BEM and neural networks

Antes, Horst, 1936-, Stavroulakis Georgios

Simple record


URIhttp://purl.tuc.gr/dl/dias/D2A749E3-DD41-4A1F-96BD-B0D8DECD151B-
Identifierhttps://doi.org/10.1007/s004660050264-
Languageen-
Extent12 pagesen
TitleNondestructive elastostatic identification of unilateral cracks through BEM and neural networksen
CreatorAntes, Horst, 1936-en
CreatorStavroulakis Georgiosen
CreatorΣταυρουλακης Γεωργιοςel
PublisherSpringer Verlagen
Content SummaryAn inverse problem in nonlinear elastostatics is considered which concerns the identification of unilateral contact cracks by means of boundary measurements for given static loadings. Highly nonlinear structural behaviour like closed cracks can hardly be identified. In this case, the analysis of more than one loading cases is proposed and tested in this paper. The direct problem is modelled by using a direct multiregion boundary element formulation. The arising linear complementarity problem is solved explicitly by a pivoting (Lemke) technique. In view of the complexity of the inverse problem, a neural network based identification approach is adopted which uses feed-forward multilayer neural networks trained by back-propagation, error-driven supervised training. The applicability of the method is demonstrated by some numerical examples.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-11-
Date of Publication1997-
Subject--Cases, clinical reports, statisticsen
Subject--Statistical dataen
Subjectstatisticsen
Subjectcases clinical reports statisticsen
Subjectstatistical dataen
Bibliographic CitationG. E. Stavroulakis, H. Antes ,"Nondestructive elastostatic identification of unilateral cracks through BEM and neural networks ,"Comput. Mech. vol. 20,no.5, pp. 439-451,1997.doi:10.1007/s004660050264en

Services

Statistics