Modeling Confinement Efficiency of Reinforced Concrete Columns with Rectilinear Transverse Steel Using Artificial Neural Networks
Publication: Journal of Structural Engineering
Volume 129, Issue 6
Abstract
Artificial neural networks have attracted considerable attention and have shown promise for modeling complex nonlinear relationships. This paper explores the use of artificial neural networks in predicting the confinement efficiency of concentrically loaded reinforced concrete (RC) columns with rectilinear transverse steel. Fifty-five experimental test results were collected from the literature of square columns tested under concentric loading. A multilayer-functional-link neural network was used for training and testing the experimental data. A comparison study between the neural network model and four parametric models is also carried out. It was found that the neural network model could reasonably capture the underlying behavior of confined RC columns. Moreover, compared with parametric models, the neural network approach provides better results. The close correlation between experimental and calculated values shows that neural network-based modeling is a practical method for predicting the confinement efficiency of RC columns with transverse steel because it provided instantaneous result once it is properly trained and tested.
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References
Chen, H. M., Tsai, K. H., Qi, G. Z., Yang, J. C. S., and Amini, F.(1995). “Neural network for structural control.” J. Comput. Civ. Eng., 9(2), 168–176.
Consolazio, G. R.(2000). “Iterative equation solver for bridge analysis using neural networks.” Comput.-Aided Civ. Infrastr. Eng., 15(2), 107–119.
Cusson, D., and Paultre, P.(1994). “High-strength concrete columns confined by rectangular ties steel.” J. Struct. Eng., 120(3), 783–804.
Cusson, D., and Paultre, P.(1995). “Stress-strain model for confined high-strength concrete.” J. Struct. Eng., 121(3), 468–477.
Dayhoff, J. (1990). Neural networks architecture, Van Nostrand Reinhold, New York.
Eldin, N. N., and Senouci, A. B.(1995). “A pavement condition-rating model using back propagation neural networks.” Microcomput. Civ. Eng., 10, 433–441.
Elkordy, M. F., Chang, K. C., and Lee, G. C.(1993). “Neural networks trained by analytically simulated damage states.” J. Comput. Civ. Eng., 7(2), 130–145.
Fausset, L. (1994). Fundamental of neural networks, Prentice-Hall, Englewood Cliffs, N.J.
Ghaboussi, J., Garrett, J. H., and Wu, X.(1991). “Knowledge-based modeling of material behavior with neural networks. ” J. Eng. Mech., 117(1), 132–153.
Goh, A. T. C.(1994). “Seismic liquefaction potential assessed by neural networks.” J. Geotech. Eng., 120(9), 1467–1480.
Goh, A. T. C.(1995). “Neural Networks for evaluating CPT calibration chamber test data.” Microcomput. Civ. Eng., 10, 147–151.
Hajela, P., and Berke, L.(1991). “Neurobiological computational models in structural analysis and design.” Comput. Struct., 41(4), 657–667.
Hopfield, J. J.(1982). “Neural network and physical systems with emergent collective computational abilities.” Proc., Natl. Acad. Sci. U.S.A., 79, 2554–2558.
Juang, C. H., Lu, P. C., and Chen, C. J.(2002). “Predicting geotechnical parameters of sands from CPT measurements using neural networks.” Comput.-Aided Civ. Infrastr. Eng., 16(2), 126–142.
Mukherjee, A., Deshpande, J. M., and Anmada, J.(1996). “Prediction of buckling load of columns using artificial neural networks.” J. Struct. Eng., 122(11), 1385–1387.
Murray, S. (1993). Neural networks for statistical modeling, Van Nostrand Reinhold, New York.
Park, R., Priestly, M. J. N., and Gill, W. D.(1982). “Ductility of square-confined concrete columns.” J. Struct. Eng., 108(4), 929–950.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). “Learning internal representation by error propagation.” Parallel distributed processing, Vol. 1, D. E. Rumelhart and J. L. McClelland, eds., MIT Press, Cambridge, Mass., 318–362.
Schalkoff, R. J. (1997). Artificial neural networks, McGraw-Hill, Singapore.
Sheikh, S. A., and Uzumeri, S. M.(1980). “Strength and ductility of tied concrete columns.” J. Struct. Div. ASCE, 106(5), 1079–1102.
Sheikh, S. A., and Uzumeri, S. M.(1982). “Analytical model for concrete confinement in tied columns.” J. Struct. Div. ASCE, 108(12), 2703–2722.
Welstead, S. T. (1994). Neural network and fuzzy logic applications in C/C++, Wiley, New York.
Yeh, I. C. (1997). Application of neural network, The Ru-Lin Companies, Inc., Taipei (in Chinese).
Yeh, I. C.(1999). “Design of high-performance concrete mixture using neural networks and nonlinear programing.” J. Comput. Civ. Eng., 13(1), 36–42.
Yong, Y. K., Nour, M. G., and Nawy, E. G.(1988). “Behavior of laterally confined high-strength concrete under axial loads.” J. Struct. Eng., 114(2), 332–351.
Zhao, Z., and Ren, L.(2002). “Failure criterion of concrete under triaxial stresses using neural networks.” Comput-Aided Civ. Infrastr. Eng., 17(1), 68–73.
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Published In
Journal of Structural Engineering
Volume 129 • Issue 6 • June 2003
Pages: 775 - 783
Copyright
Copyright © 2003 American Society of Civil Engineers.
History
Received: Dec 6, 2001
Accepted: Jun 24, 2002
Published online: May 15, 2003
Published in print: Jun 2003
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