Neural networks and cold-formed steel design

E.M.A. El-Kassas, R.I. Mackie, A.I. El-Sheikh

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The application of neural networks to cold-formed steel design is considered. Cold-formed steel design is more complex than hot-rolled steel design because of the large number of different section profiles, and traditional computing tools are not well-suited for dealing with this. The flexibility of neural networks makes them a suitable alternative. The paper describes the use of neural networks to predict the failure load of cold-formed sections, and the results are in good agreement with results from design codes.

    Original languageEnglish
    Title of host publicationComputational engineering using metaphors from nature
    EditorsB.H.V. Topping
    Place of PublicationEdinburgh
    PublisherCivil-Comp Press
    Pages37-43
    Number of pages7
    Volume64
    ISBN (Print)0948749660
    DOIs
    Publication statusPublished - 2000
    Event5th International Conference on Computational Structures Technology/2nd International Conference on Engineering Computational Technology - LEUVEN, Belgium
    Duration: 6 Sep 20008 Sep 2000
    http://www.civil-comp.com/conf/prog2000.htm

    Conference

    Conference5th International Conference on Computational Structures Technology/2nd International Conference on Engineering Computational Technology
    CountryBelgium
    CityLEUVEN
    Period6/09/008/09/00
    Internet address

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