Predicting fire resistance ratings of timber structures using artificial neural networks

  • Pham Thanh Tung Faculty of Building and Industrial Construction, National University of Civil Engineering, 55 Giai Phong road, Hai Ba Trung district, Hanoi, Vietnam
  • Pham Thanh Hung Faculty of Civil Engineering, Hanoi Architectural University, Nguyen Trai road, Hanoi, Vietnam

Abstract

This paper describes a method to predict the fire resistance ratings of the wooden floor assemblies using Artificial Neural Networks. Experimental data collected from the previously published reports were used to train, validate, and test the proposed ANN model. A series of model configurations were examined using different popular training algorithms to obtain the optimal structure for the model. It is shown that the proposed ANN model can successfully predict the fire resistance ratings of the wooden floor assemblies from the input variables with an average absolute error of four percent. Besides, the sensitivity analysis was conducted to explore the effects of the separate input parameter on the output. Results from analysis revealed that the fire resistance ratings are sensitive to the change of Applied Load (ALD) and the number of the Ceiling Finish Layer (CFL) input variables. On the other hand, the outputs are less sensitive to a variation of the Joist Type (JTY) parameter.

Keywords:

artificial neural networks; fire resistance; wooden floor assembly; sensitivity analysis.

Downloads

Download data is not yet available.
Published
17-04-2020
How to Cite
Tung, P. T., & Hung, P. T. (2020). Predicting fire resistance ratings of timber structures using artificial neural networks. Journal of Science and Technology in Civil Engineering (STCE) - NUCE, 14(2), 28-39. https://doi.org/10.31814/stce.nuce2020-14(2)-03
Section
Research Papers