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Efficient neural network models for structural reliability analysis and identification problems

Nikos Lagaros , Yiannis Tsompanakis, Georgios E. Stavroulakis

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URIhttp://purl.tuc.gr/dl/dias/E955958B-0F2C-47D0-8B74-655247879720-
Identifierhttps://doi.org/10.4203/ccp.82.41-
Languageen-
Extent24 pagesen
TitleEfficient neural network models for structural reliability analysis and identification problemsen
CreatorNikos Lagaros en
CreatorYiannis Tsompanakisen
CreatorGeorgios E. Stavroulakisen
Content SummaryThe objective of this paper is to investigate the efficiency of soft computing methods, in particular methodologies based on neural networks, when incorporated into the solution of computationally intensive engineering problems. Two types of applications have been investigated, namely flaw identification and structural reliability analysis. Artificial neural networks (ANNs) based metamodels are used in order to replace the time-consuming repeated structural analyses. The back propagation algorithm is employed for training the ANN, using data derived from selected analyses. The trained ANN is then used to predict the values of the necessary data. The numerical tests demonstrate the computational advantages of the proposed methodologies. en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-08-
Date of Publication2005-
Subjectengineering problemsen
Bibliographic CitationY. Tsompanakis, N. D. Lagaros,G. E. Stavroulakis ,"Efficient neural network models for structural reliability analysis and identification problems," in 2005 Eighth Intern. Conf. on the App. of Artificial Intelli.e to Civil, Struct. and Envir. Engin.,pp.1-24.doi :10.4203/ccp.82.41en

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