Determining Fire Resistance of Wooden Construction Elements through Experimental Studies and Artificial Neural Network

Authors: Sakir Tasdemir, Mustafa Altin, Gamze Fahriye Pehlivan, Ismail Saritas, Sadiye Didem Boztepe Erkis, Selma Tasdemir

Abstract:

Artificial intelligence applications are commonly used in industry in many fields in parallel with the developments in the computer technology. In this study, a fire room was prepared for the resistance of wooden construction elements and with the mechanism here, the experiments of polished materials were carried out. By utilizing from the experimental data, an artificial neural network (ANN) was modelled in order to evaluate the final cross sections of the wooden samples remaining from the fire. In modelling, experimental data obtained from the fire room were used. In the developed system, the first weight of samples (ws-gr), preliminary cross-section (pcs-mm2), fire time (ft-minute), and fire temperature (t-oC) as input parameters and final cross-section (fcs-mm2) as output parameter were taken. When the results obtained from ANN and experimental data are compared after making statistical analyses, the data of two groups are determined to be coherent and seen to have no meaning difference between them. As a result, it is seen that ANN can be safely used in determining cross sections of wooden materials after fire and it prevents many disadvantages.

Keywords: Artificial neural network, final cross-section, fire retardant polishes, fire safety, wood resistance.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099688

References:

[1] G. Koca, N. As and N. Arıoğlu, “Ahşap Dış Cephe Kaplama Elemanları”, 7. Ulusal Çatı & Cephe Sempozyumu, Yıldız Teknik Üniversitesi İstanbul, Turkey, 2013.
[2] A. Kılıç, http://www.yangin.org/, 12 May 2014.
[3] Ahşap Kaplamalar ve Uygulama Esasları, http://www.cs.sakarya.edu.tr/ sites/ivural/file/AHSAP-KAPLAMALAR.pdf, 15 May 2014.
[4] R. Stevens, S.D. Es van, R. Bezemer and A. Kranenbarg, “The Structure Activity Relationship of Fire Retardant Phosphorus Compounds in Wood”, Polymer Degradation and Stability, 91, 832-841, 2006.
[5] Yangın Geciktirici Cila Sistemleri, http://www.hemel.com.tr/tr/urunler/ default.aspx?lsn=1&KatID=1020501&UAd=Yangin-Geciktirici-Cila- Sistemleri, 15 May 2014.
[6] Ş. Tasdemir, S. Neşeli and S. Yaldız, “Prediction of surface roughness on turning with Artificial Neural Network”, Journal of Engineering and Architecture Faculty of Eskişehir Osmangazi University 22(9), 65-75, 2009
[7] S. Tasdemir, S. Neseli, I. Saritas and S. Yaldiz, “Prediction of Surface Roughness Using Artificial Neural Network in Lathe”, CompSysTech’08, IIIB.6-1- IIIB.6-8 pp., Gabrovo, Bulgaria, Haziran 2008.
[8] S. Tasdemir, I. Saritas, M. Ciniviz, C. Cinar, and N. Allahverdi, “Application of artificial neural network for definition of a gasoline engine performance”, 4th International Advanced Technologies Symposium, Konya, Turkey, 28–30 September, pp. 1030–1034, 2005.
[9] Y. Okayama, “A primitive study of a fire detection method controlled by artificial neural net”, Fire Saf J, 17, 535-553, 1991.
[10] S.L. Rose-Peherson, R.E. Shaffer, S.J. Hart, F.W. Williams, D.T. Gottuk, B.D. Strehlen and A. Hill, “Multi-criteria fire detection systems using a probabilistic neural network”, Sensors and Actuators, B: Chemical, 69, 325-335, 2000.
[11] 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.
[12] A. M. Fernandes, A. B. Utkin, A. V. Lavrov and R. M. Vilar, "Development of Neural Network Committee Machines for Forest Fire Detection Using Lidar," Pattern Recognition, 37, 10, 2039-2047, 2004.
[13] 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.
[14] W, Xue-gui, L. Siu-ming and Z. He-ping, “Influence of Feature Extraction Duration and Step Size on ANN based Multisensor Fire Detection Performance”, Procedia Engineering 52, 413-421, 2013.
[15] ISO 14001, “Environmental management systems-Requirements with guidance for use”, 2004.
[16] M. Altin, “Determining behaviors of fire doors with thermal camera and traditional methods comparatively”, Energy Education Science and Technology Part A: Energy Science and Research, 30(1), 465-474, 2012.