Application of ANN Modelling of Fire Door Resistance

Abstract

Fire doors are compulsorily used in every kind of building nowadays. The determination of fire doors’ resistance in which kind of buildings is also essential. This determination is needed to be watched through the experimental works done. Computer technologies and applications are commonly used in many fields in industry. In this study, by using the data obtained as a result of experiments made in order to determine the resistance of fire doors, artificial neural network (ANN) model was developed. With this model, it is aimed to evaluate the inner temperature of fire room having an important role in resistance of the fire door. In the developed system, temperature values belonging to thermocouples on the door (Top Left, Top  Right, Middle Left, Middle Right, Bottom Left, Bottom Right (^oC) and time (minute) were taken as input parameters and in-room temperature (^oC) was taken as output parameters. When the results obtained from ANN and experimental data are compared, it is determined that two groups of data were coherent. It is shown that ANN can be safely used in the determination of fire door resistance. 

Keywords

• Artificial neural network,Fire doors,In-Room temperature,Computer modelling

Kaynakça

1. Tasdemir S., Altin M., Pehlivan G. F., Sarıtaş İ., B. Erkis Ş. D., Tasdemir S., “Determining Fire Resistance of Wooden Construction Elements through Experimental Studies and Artificial Neural Network.” World Academy of Science, Engineering and Technology, International Science Index 97, International Journal of Chemical, Nuclear, Materials and Metallurgical Engineering, (2015),9 (1), 209-213.
2. J. A. Capote, D. Alvear e.g.. “Assessment of Physical Phenomena Associated to Fire Doors During Standard”, Tests Fire Technology, 2012, DOI: 10.1007/s10694-012-0270-0
3. Altin M., “Determining behaviors of fire doors with thermal camera and traditional methods comparatively”, Energy Education Science and Technology Part A: Energy Science and Research, 2012, (Spec .Iss.1 ) pp. 465-474.
4. Kılıç A.,http://www.yangin.org/dosyalar/yangin_kapilari.pdf, 20 April 2015.
5. Barreira, E. and De Freitas, V.P., “Evaluation of building materials using infrared thermography”, Construction and Building Materials"; 2007, 21; 218–224.
6. Taşdemir Ş., Neşeli S., Sarıtaş İ., Yaldız S., Prediction of surface roughness using artificial neural network in Lathe. CompSysTech’08, Gabrovo, Bulgaristan, 2008.
7. Tasdemir, S., Saritas, I., Ciniviz, M., Cinar, C., Allahverdi, N. Application of artificial neural network for definition of a gasoline engine performance, in: 4th International Advanced Technologies Symposium, Konya, Turkey, 28–30 September, pp. 1030–1034, 2005.
8. TS EN 1634-1 (2010), “Fire resistance and smoke control tests for door, shutter and openable window assemblies and elements of building hardware - Part 1: Fire resistance tests for doors, shutters and openable windows”, Turkish Standard, Ankara.

9. Altin M., “Determining Fire Door Resistance Through Infrared Thermography”, International Conference on Advanced Technology & Sciences (ICAT'14) 363-366 pp., Antalya, Turkey, 12-15 August 2014.
10. K. Dinçer, Ş. Taşdemir, Ş. Başkaya, İ. Üçgül, B.Z. Uysal, 2008. Fuzzy Modeling of Performance of Counterflow Ranque-Hilsch Vortex Tubes with Different Geometric Constructions, Numerical Heat Transfer Part B: Fundamentals, 54, 499-517.
11. W.M. Lee, K.K. Yuen, S.M. Lo, K.C. Lam and G.H. Yeoh, “A novel artificial neural network fire model for prediction of thermal interface location in single compartment fire”, Fire Safety Journal, 39, 67-87, 2004.
12. R. Jolivet, T. J. Lewis, and W. Gerstner, “Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy”, J Neurophysiol 92, 959-976, 2004.