Counterpropagation Neural Network Model for Steel Girder Bridge Structures
Publication: Journal of Bridge Engineering
Volume 9, Issue 1
Abstract
Bridge rating is based on the method of design: working stress design (WSD) or load factor design (LFD). A large number of older bridges were rated based on the WSD code. These WSD-based bridge ratings now have to be converted to the LFD-based rating. The LFD-based rating of steel bridges requires a detailed description of the steel girder’s geometric properties, which may not be available. In this article, a counterpropagation neural network model is presented for estimating the detailed section properties of steel bridge girders needed in the LFD-based rating based on the three cross-sectional properties used in the WSD-based rating of bridges: cross-section area, moment of inertia, and section modulus. It is demonstrated that, with proper training of the network using both standard wide-flange shape and representative plate girder data, the proposed model can generate the detailed section properties needed for LFD-based rating of steel bridges quite accurately. The result of this research can be used in an intelligent decision support system (IDSS) to help bridge engineers convert a WSD-based bridge rating to the LFD-based rating.
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References
Adeli, H.(2001). “Neural networks in civil engineering, 1989–2000.” Comput. Aided Civ. Infrastruct. Eng., 16(2), 126–147.
Adeli, H., and Hung, S. L. (1995). Machine learning—neural networks, genetic algorithms, and fuzzy systems, Wiley, New York.
Adeli, H., and Karim, A. (2001). Construction scheduling, cost optimization, and management: a new model based on neurocomputing and object technologies, E&FN Spon, London.
Adeli, H., and Park, H. S.(1995). “Counterpropagation neural networks in structural engineering.” J. Struct. Eng., 121(8), 1205–1212.
Adeli, H., and Park, H. S. (1998). Neurocomputing for design automation, CRC Press, Boca Raton, Fla.
American Association of State Highway and Transportation Officials (AASHTO). (1994). Bridge analysis and rating system, BARS-PC, release 5.5, mod 3.2, user manuals I and II, Washington, D.C.
American Institute of Steel Construction (AISC). (1995). Manual of steel construction: allowable stress design, Chicago.
American Institute of Steel Construction (AISC). (1998). Manual of steel construction: load and resistance factor design, Chicago.
DeSieno, D. (1988). “Adding a conscience to competitive learning.” Proc., IEEE Int. Conf. on Neural Networks, Vol. 1, Institute of Electrical and Electronics Engineering, New York, 117–124.
Grossberg, S. (1982). Studies of mind and brain, Reidel, Boston.
Hecht-Nielsen, R.(1987). “Counterpropagation networks.” Appl. Opt., 26(23), 4979–4985.
Kohonen, T. (1998). Self-organized and associative memory, Springer, New York.
Mehrotra, K., Mohan, C. K., and Ranka, S. (1997). Elements of artificial neural networks, MIT Press, Cambridge, Mass.
Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 9 • Issue 1 • January 2004
Pages: 55 - 65
Copyright
Copyright © 2004 American Society of Civil Engineers.
History
Received: Aug 24, 2001
Accepted: Sep 27, 2002
Published online: Dec 15, 2003
Published in print: Jan 2004
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