Steel and Composite Structures

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Fire resistance prediction of slim-floor asymmetric steel beams using single hidden layer ANN models that employ multiple activation functions

  • Asteris, Panagiotis G. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Maraveas, Chrysanthos (Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens) ;
  • Chountalas, Athanasios T. (Computational Mechanics Laboratory, School of Pedagogical and Technological Education) ;
  • Sophianopoulos, Dimitrios S. (Department of Civil Engineering, University of Thessaly) ;
  • Alam, Naveed (FireSERT, School of Built Environment, Ulster University)
  • Received : 2022.04.28
  • Accepted : 2022.09.15
  • Published : 2022.09.25
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Abstract

In this paper a mathematical model for the prediction of the fire resistance of slim-floor steel beams based on an Artificial Neural Network modeling procedure is presented. The artificial neural network models are trained and tested using an analytical database compiled for this purpose from analytical results based on FEM. The proposed model was selected as the optimum from a plethora of alternatives, employing different activation functions in the context of Artificial Neural Network technique. The performance of the developed model was compared against analytical results, employing several performance indices. It was found that the proposed model achieves remarkably improved predictions of the fire resistance of slim-floor steel beams. Moreover, based on the optimum developed AN model a closed-form equation for the estimation of fire resistance is derived, which can prove a useful tool for researchers and engineers, while at the same time can effectively support the teaching of this subject at an academic level.

Keywords

References

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