Using neural networks to predict the design load of cold-formed steel compression members

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

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)

    Abstract

    The paper considers the use of neural networks to predict the failure load of cold-formed steel compression members. The objective is to provide a fast method of predicting the failure load, so that the method can be used in other design algorithms, such as optimisation routines. Three types of symmetric sections are considered, and the results of neural network predictions compared with results from BS5950 Part 5. The results are in good agreement with the results from design codes. Moreover, a trained neural network gives the results significantly more quickly than a computer implementation of the code. (C) 2002 Civil-Comp Ltd and Elsevier Science Ltd. All rights reserved.

    Original languageEnglish
    Article numberPII S0965-9978(02)00051-0
    Pages (from-to)713-719
    Number of pages7
    JournalAdvances in Engineering Software
    Volume33
    Issue number7-10
    DOIs
    Publication statusPublished - 2002
    Event2nd International Conference on Engineering Computational Technology/5th International Conference on Computational Structures Technology - LEUVEN, Belgium
    Duration: 6 Sep 20008 Sep 2000
    http://www.civil-comp.com/conf/cst2000.htm

    Keywords

    • steel structures
    • neural networks
    • structural design
    • cold-formed steel

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