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

  • Authors:

  • E. M. A. El-Kassas;R. I. Mackie;A. I. El-Sheikh

  • Affiliations:

  • Department of Civil Engineering, Structural Engineering Research Group, University of Dundee, Dundee DD1 4HN, UK;Department of Civil Engineering, Structural Engineering Research Group, University of Dundee, Dundee DD1 4HN, UK;Department of Civil Engineering, Structural Engineering Research Group, University of Dundee, Dundee DD1 4HN, UK

  • Venue:
  • Advances in Engineering Software - Engineering computational technology
  • Year:
  • 2002

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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.