Neural Network Modeling of Confined Compressive Strength and Strain of Circular Concrete Columns
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VIEW THE REPLYPublication: Journal of Structural Engineering
Volume 129, Issue 4
Abstract
The application of artificial neural networks (ANN) to predict the confined compressive strength and corresponding strain of circular concrete columns is explored. Using available data from past experiments, an ANN model with input parameters consisting of the unconfined compressive strength, core diameter, column height, yield strength of lateral reinforcement, volumetric ratio of lateral reinforcement, tie spacing, and longitudinal steel ratio was found to be acceptable in predicting the confined compressive strength and corresponding strain of circular concrete columns subject to limitations in the training data. The study shows the importance of validating the ANN models in simulating physical processes especially when data are limited. The ANN model was also compared to some analytical models and was found to perform well.
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References
Abu Kiefa, M. A.(1998). “General regression neural networks for driven piles in cohesionless soils.” J. Geotech. Geoenviron. Eng., 124(12), 1177–1185.
Agrawal, G., Chameau, J. A., and Bourdeau, P. L. (1997). “Assessing the liquefaction susceptibility at a site based on information from penetration test.” Artificial neural networks for civil engineers: Fundamentals and applications, N. Kartam, I. Flood, and J. H. Garrett, eds., ASCE, New York, 185–214.
Carpenter, W. C., and Barthelemy, J.(1994). “Common misconceptions about neural networks as approximators.” J. Comput. Civ. Eng., 8(3), 345–358.
Carpenter, W. C., and Hoffman, M. E. (1995). “Training backprop neural networks.” AI Expert, March, 30–33.
Dadios, E. P. (1998). “Neural network application to pattern recognition.” 2nd Pacific Asia Conf. on Mechanical Engineering, Technological Univ. of the Philippines, Manila, Philippines.
Freeman, J. A., and Skapura, D. M. (1991). Neural networks: Algorithms, applications, and programming techniques, Addison-Wesley, Reading, Mass., 89–125.
Garson, G. D. (1991). “Interpreting neural network connection weights.” AI Expert, April, 47–51.
Goh, A. T. C.(1994). “Seismic liquefaction potential assessed by neural networks.” J. Geotech. Eng., 120(9), 1467–1480.
Goh, A. T. C.(1995). “Modeling soil correlations using neural networks.” J. Comput. Civ. Eng., 9(4), 275–278.
Hoshikuma, K., Kawashima, K., Nagaya, K., and Taylor, A. W.(1997). “Stress-strain model for confined reinforced concrete in bridge piers.” J. Struct. Eng., 123(5), 624–633.
Kasperkiewicz, J., Racz, J., and Dubrawski, A.(1995). “HPC strength prediction using artificial neural network.” J. Comput. Civ. Eng., 9(4), 279–284.
Mander, J. B., Priestley, M. J. N., and Park, R.(1988a). “Theoretical stress-strain model for confined concrete.” J. Struct. Eng., 114(8), 1804–1826.
Mander, J. B., Priestley, M. J. N., and Park, R.(1988b). “Observed stress-strain behavior of confined concrete.” J. Struct. Eng., 114(8), 1827–1849.
Penelis, G. G., and Kappos, A. J. (1997). Earthquake-resistant concrete structures, E&FN Spon, London, Sec. 7.4, 177–196.
Saatcioglu, M., and Razvi, S. R.(1992). “Strength and ductility of confined concrete.” J. Struct. Eng., 118(6), 1590–1607.
Sakai, J. (2001). “Effect of lateral confinement of concrete and varying axial load on seismic response of bridges.” Doctor of Engineering Dissertation, Dept. of Civil Engineering, Tokyo Institute of Technology, Tokyo.
Sakai, J., Kawashima, K., Une, H., and Yoneda, K.(2000). “Effect of tie spacing on stress-strain relation of confined concrete.” J. Struct. Eng., 46A(3), 757–766.
Sakai, K., and Sheikh, S. A.(1989). “What do we know about confinement in reinforced concrete columns?” ACI Struct. J., 86(2), 192–207.
Teh, C. I., Wong, K. S., Goh, A. T. C., and Jaritngam, S.(1997). “Prediction of pile capacity using neural networks.” J. Comput. Civ. Eng., 11(2), 129–138.
Tsoukalas, L. H., and Uhrig, R. E. (1997). Fuzzy and neural approaches in engineering, Wiley, New York, 385–405.
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Copyright © 2003 American Society of Civil Engineers.
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Received: Apr 18, 2001
Accepted: Jul 8, 2002
Published online: Mar 14, 2003
Published in print: Apr 2003
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