Advanced Steel Construction

Vol. 5, No. 4, pp. 452-464 (2009)



K.S. Al-Jabri 1,*, S.M. Al-Alawi 2, A.H. Al-Saidy 1 and A.S. Alnuaimi 1

1 Department of Civil and Architectural Engineering, College of Engineering, Sultan Qaboos University, Oman

2 Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Oman

*(Corresponding author: E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.)

Received: 9 April 2008; Revised: 14 July 2008; Accepted: 29 July 2008




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This paper presents an artificial neural networking (ANN) model developed to predict the behaviour of semi-rigid bare-steel joints at elevated temperature. Data for three flush end-plate and one flexible end-plate joints were considered.   Sixteen parameters which included geometry of the joint’s components, material properties of the joint, joint’s temperature and the applied moment were used as the input variables for the model whilst the joint’s rotation was the main output parameter.   Data from experimental fire tests were used for training and testing the model.   In total, fifteen different test results were evaluated with 331 and 61 cases were used for training and testing the developed model, respectively. The model predicted values were compared with actual test results. The results obtained indicated that the model can predict the moment-rotation behaviour in fire with very high accuracy. The coefficients of determination (R2) for training and validation of the model were 0.964 and 0.956, respectively.



Bare-steel, flush end-plate, flexible end-plate, semi-rigid joints, artificial neural network, fire, elevated temperature, rotation


[1] Al-Jabri, K.S., Burgess, I.W., Lennon, T. and Plank, R.J., “Moment-Rotation-Temperature Curves for Semi-Rigid Joints”, Journal of Constructional Steel Research, 2005, Vol. 61, pp. 281-303.

[2] Stavroulakis, G.E. and Abdalla, K.M., “A Systematic Neural Network Classificator in Mechanics. An Application in Semi-rigid Steel Joints”, International Journal for Engineering Analysis and Design, 1994, Vol. 1, pp. 279-292.

[3] Stavroulakis, G.E., Avdelas, A.V., Abdalla, K.M. and Panagiotopoulos, P.D., “A Neural Network Approach to the Modelling, Calculation and Identification of Semi-rigid Connections in Steel Structures”, Journal of Constructional Steel Research, 1997, Vol. 44, No. 1-2, pp. 91-105.

[4] Anderson, D., Hines, E.L., Arthur, S.J. and Eiap, E.L., “Application of Artificial Neural Networks to the Prediction of Minor Axis Steel Connections”, Computers and Structures, 1997, Vol. 63, No. 4, pp. 685-692.

[5] Dermitas, B., De Santiago, E. and O'leary, J.R., “Classification of Steel Semi-rigid Connections by Neural Networks”, Structures 2004-Building on the Past: Securing the future, 2004. Nashville, Tennessee, USA.

[6] Al-Khaleefi, A.M., Terro, M.J., Alex, A.P. and Wang, Y., “Prediction of Fire Resistance of Concrete Filled Tubular Steel Columns using Neural Networks”, Fire Safety Journal, 2002, Vol. 37, pp. 339-352.

[7] Rumelhart, D. and McClelland, J., “PDP Research Group. Parallel Distributed Processing, Explorations in the Microstructure of Cognition, Vol. 11: Foundations”, Cambridge, MA, MIT Press/Bradford Books, 1988.

[8] Stanley, J., “Introduction to Neural Networks”, 3rd Edition, Sierra Madre, California Scientific Software, California, 1990.

[9] Simpson, P.K., “Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations”, 1st Edition, Elmsford, NY, Pergamon Press, Inc., 1990.

[10] Guzelbey, I.H., Cevik, A. and Gögüs, M.T., “Prediction of Rotation Capacity of Wide Flange Beams using Neural Networks”, Journal of Constructional Steel Research, 2006, Vol. 62, pp. 950-961.

[11] Guzelbey, I.H., Cevik, A., and Erklig, A., “Prediction of Web Crippling of Cold-Formed Steel Sheetings using Neural Networks”, Journal of Constructional Steel Research, 2006; Vol. 62, pp. 962-973.

[12] Leston-Jones, L.C., “The Influence of Semi-rigid Connections on the Performance of Steel Framed Structures in Fire”, PhD, Thesis, University of Sheffield, UK, 1997.

[13] Garson, G.D., “Interpreting Neural Network Connection Weights”, AI Expert; 1991, Vol. 6, No. 7, pp. 47-51.

[14] Goh, A.T.C., “Back-Propagation Neural Networks for Modelling Complex Systems”, Artificial Intelligence in Engineering, 1995, Vol. 9, pp. 143-151.

[15] Hecht-Nielsen, P., “Theory of the Back Propagation Neural Network”, Proceeding of International Conference on Neural Networks. Washington D.C., 1989, pp. 593-605.

[16] Carpenter, W.C. and Hoffman, M.E., “Training Back Prop Neural Networks, AI Expert, 1995, Vol. 10, pp. 30–33.

[17] NeuroShellä., “Neural Network Shell Program”, 4th Edition. Ward Systems Group, Inc., Frederick, MD, USA, 1991.