Prediction of fire resistance of concrete filled tubular steel columns using neural networks

Abdullateef M. Al-Khaleefi, Mohammad J. Terro, Alex P. Alex, Yong Wang

    Research output: Contribution to journalArticlepeer-review


    A functional relationship between the fire resistance of a concrete filled steel column and the parameters which cause the fire resistance is represented using an artificial neural network. Experimental data obtained from previous laboratory fire tests are used for training the neural network model. The model predicted values are compared with actual test results. The results indicate that the model can predict the fire resistance with adequate accuracy required for practical design purpose. The developed neutral network can be used to predict the fire resistance of similar columns under fire by observing various factors influencing the resistance such as: (a) structural factors, (b) material factors, and (c) loading conditions. The structural engineer is required to provide the magnitude of these influencing factors as inputs to the neural network and the network will predict the fire resistance, based on the combined effect of these factors. This system can be used by structural engineers to predict the resistance of fire in similar concrete filled steel columns without conducting costly fire tests, by using the known parameters such as column dimensions, column height, and loading conditions. © 2002 Published by Elsevier Science Ltd.
    Original languageEnglish
    Pages (from-to)339-352
    Number of pages13
    JournalFire Safety Journal
    Issue number4
    Publication statusPublished - Jun 2002


    • Artificial neural networks
    • Construction
    • Fire resistance
    • Structural analysis


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