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A backpropagation neural network model for semi-rigid steel connections

Stavroulakis Georgios, K. M. Abdalla

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URIhttp://purl.tuc.gr/dl/dias/5027A657-10BA-455C-A22E-0E706116BB96-
Identifierhttps://doi.org/10.1111/j.1467-8667.1995.tb00271.x-
Languageen-
Extent10 pagesen
TitleA backpropagation neural network model for semi-rigid steel connectionsen
CreatorStavroulakis Georgiosen
CreatorΣταυρουλακης Γεωργιοςel
CreatorK. M. Abdallaen
Content SummaryThe analysis of semirigid steel structure connections based on exact theoretical modeling, which is demanding and time consuming if all the nonlinear parameters of the problem are taken into account, can be avoided provided that enough experimental measurements exist and an appropriate predictor can be constructed from them. A supervised learning backpropagation neural network approach is proposed in this paper for the construction of this model free predictor. A number of experimental momentrotation curves for single-angle and single-plate beam-to-column connections are used in this paper to train the neural network. The trained network provides us with an estimator for the mechanical behavior of the steel structure connection element.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 Publication1995-
SubjectEngineering, Mechanicalen
Subjectmechanical engineeringen
Subjectengineering mechanicalen
Bibliographic CitationK. M. Abdalla , G. E. Stavroulakis ," A backpropagation neural network model for semi-rigid steel connections,"Computer-Aided Civil and Infrast. Engin., vol. 10, no. 2,pp. 77–87, March 1995.doi:10.1111/j.1467-8667.1995.tb00271.xen

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