Determining Fire Resistance of Wooden Construction Elements through Experimental Studies and Artificial Neural Network
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.
[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.
[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.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:69259", author = "Sakir Tasdemir and Mustafa Altin and Gamze Fahriye Pehlivan and Ismail Saritas and Sadiye Didem Boztepe Erkis and Selma Tasdemir", title = "Determining Fire Resistance of Wooden Construction Elements through Experimental Studies and Artificial Neural Network", 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.", volume = "9", number = "1", pages = "209-5", }
