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Steel and Composite Structures
  Volume 44, Number 6, September25 2022 , pages 769-788
DOI: https://doi.org/10.12989/scs.2022.44.6.769
 


Fire resistance prediction of slim-floor asymmetric steel beams using single hidden layer ANN models that employ multiple activation functions
Panagiotis G. Asteris, Chrysanthos Maraveas, Athanasios T. Chountalas, Dimitrios S. Sophianopoulos and Naveed Alam

 
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.
 
Key Words
    activation functions; artificial neural networks; slim-floor steel beams; fire resistance; soft computing
 
Address
Panagiotis G. Asteris:Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece

Chrysanthos Maraveas:Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, Greece

Athanasios T. Chountalas:Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece

Dimitrios S. Sophianopoulos and Naveed Alam: Department of Civil Engineering, University of Thessaly, Volos, Greece 4 FireSERT,
School of Built Environment, Ulster University, Belfast, UK
 

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