Neural Network Prognostic Model for Predicting the Fire Resistance of Eccentrically Loaded RC Columns


Article Preview

Using the concept of the artificial neural networks and the results of the performed numerical analyses as input parameters, the prediction model for defining the fire resistance of RC columns incorporated in walls and exposed to standard fire from one side, has been made. A short description of the numerical analyses of columns exposed to standard fire ISO 834, conducted by the computer software FIRE are presented in this paper. The software is capable of predicting the nonlinear response of reinforced concrete elements and plane frame structures subjected to fire loading, carrying out the nonlinear transient heat flow analysis and nonlinear stress-strain response associated with fire.



Edited by:

Bale V. Reddy, Shishir Kumar Sahu, A. Kandasamy and Manuel de La Sen




M. Lazarevska et al., "Neural Network Prognostic Model for Predicting the Fire Resistance of Eccentrically Loaded RC Columns", Applied Mechanics and Materials, Vol. 627, pp. 276-282, 2014

Online since:

September 2014




* - Corresponding Author

[1] V.V. Belov, K.V. Semenov and I.A. Renev: Fire resistance of reinforced concrete constructions: models and methods of calculation. Magazine of Civil Engineering, vol. 6 (2011) pp.58-61.

[2] D.E. Kolomiyczev, A.O. Rodicheva and V.A. Ry'bakov: Estimation of fire resistance of inserted floor fragment based on steel C-shaped profiles. Magazine of Civil Engineering, vol. 8 (2011) pp.32-37.

[3] A.V. Ulybin and S.D. Fedorov: Ultrasonic method used for the estimation of damage zone in reinforced concrete after the fire, Magazine of Civil Engineering, vol. 7 (2009) pp.38-40.

[4] D.V. Kurlapov: Influence of high temperatures of fire on building structures. Magazine of Civil Engineering, vol. 4 (2009) pp.41-43.

[5] V.A. Kazakova, A.G. Tereshchenko and E.S. Nedviga: The high-rise buildings fire safety. Construction of Unique Buildings and Structures, vol. 4 (2014) pp.38-56.

[6] I. Flood: Simulating the construction process using neural networks. Proceedings of the 7th ISARC – International Association for Automation and Robotics in Construction, Bristol, United Kingdom, (1990) pp.374-382.

[7] DS. Jeng, DH. Cha and M. Blumenstein: Application of Neural Networks in Civil Engineering Problems. Proceedings of the International Conference on Advances in the Internet, Processing, Systems and Interdisciplinary Research (IPSI-2003).

[8] M. Lazarevska, M. Knezevic, M. Cvetkovska, A.T. Gavriloska and T. Samardzioska: Fire-resistance prognostic model for reinforced concrete columns [Prognostički model za odredivanje požarne otpornosti AB stupova] (2012).


[9] A. Kaklauskas, J. Rute, E.K. Zavadskas, A. Daniunas, V. Pruskus, J. Bivainis, R. Gudauskas and V. Plakys: Passive House model for quantitative and qualitative analyses and its intelligent system. Energy and Buildings, 50, (2012), pp.7-18.


[10] M. Cvetkovska: Nonlinear stress strain behavior of RC elements and plane frame structures exposed to fire. Ph. D. thesis, Civil Engineering Faculty in Skopje, Sts Cyril and Methodius University, Macedonia, (2002).

[11] D. Nemova, V. Murgul, A. Golik, E. Chizhov, V. Pukhkal and N. Vatin: Reconstruction of administrative buildings of the 70s: the possibility of energy modernization, Journal of Applied Engineering Science, Vol. 12 (1), (2014), pp.37-44.


[12] I. Flood and N. Kartam: Neural networks in civil engineering II: Systems and application. Journal of Computing in Civil Engineering 8, no. 2 (1994) pp.149-162.

[13] M. Knežević: Risk management of civil engineering projects. Ph. D. thesis, Civil Engineering Faculty, University in Belgrade, Serbia (2005).

[14] I. Flood and P. Christophilos: Modeling construction processes using artificial neural networks Automation in Construction 4, (4) (1996) pp.307-320.

[15] M. Lazarevska, A.T. Gavriloska, M. Knezevic, T. Samardzioska and M. Cvetkovska. Neural network prognostic model for RC beams strengthened with CFRP strips. Journal of Applied Engineering Science, vol. 10 (2012) pp.27-30.

[16] V. Murgul: Features of energy efficient upgrade of historic buildings (illustrated with the example of Saint-Petersburg), Journal of Applied Engineering Science, Vol. 12 (1), (2014), рp 1-10.