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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 74
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TO CIVIL AND STRUCTURAL ENGINEERING
Edited by: B.H.V. Topping and B. Kumar
Paper 26

Developments in the Use of Neural Nets for Truck Weigh-in-Motion on Steel Bridges

I. Flood+, R.R.A. Issa+ and N. Kartam*

+Rinker School, University of Florida, Gainesville, Florida, United States of America
*Department of Civil Engineering, University of Kuwait, Safat, Kuwait

Full Bibliographic Reference for this paper
I. Flood, R.R.A. Issa, N. Kartam, "Developments in the Use of Neural Nets for Truck Weigh-in-Motion on Steel Bridges", in B.H.V. Topping, B. Kumar, (Editors), "Proceedings of the Sixth International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 26, 2001. doi:10.4203/ccp.74.26
Keywords: neural network, axle loads, binary networks, bridge girder strain, dynamic bridge loading, radial-Gaussian network, self-organizing network, truck attribute estimation, weigh-in-motion.

Summary
The paper reports on the latest developments of a neural network-based method of accurately estimating truck attributes (such as axle loads) from strain response readings taken from the bridge over which the truck is traveling[1,2,3,4]. The approach is designed to remove the need for intrusive devices (such as tape switches)[5] on the deck of the bridge to obtain such data so as to provide a convenient and viable means of collecting bridge loading statistics.

Specifically, this paper compares the performance of three radically different types of neural network used for identifying the class of truck crossing the bridge. Of the methods considered, a binary networking system is found to be the most efficient. The paper concludes with some recommendations for further study.

Several recommendations are made for future work, aimed at further improving the performance of the system. Primarily, the work here focused on simply supported steel bridges with negligible skew. It is recommended that the technique be applied to other bridge configurations, such as skewed and pre-stressed concrete structures.

References
1
I. Flood, N. Kartam, "A Binary Classifier with Applications to Poorly Defined Engineering Problems", Journal of Artificial Intelligence in Engineering Design, Analysis and Manufacturing, Cambridge University Press, New York, (12)3, 259-272, 1998. doi:10.1017/S0890060498123041
2
I. Flood, "Modeling Dynamic Engineering Processes Using Radial-Gaussian Neural Networks", Journal of Intelligent and Fuzzy Systems: Applications in Engineering and Technology", (7), 373-385, 1999.
3
N. Gagarin, I. Flood, P. Albrecht, "Computing Truck Attributes with Artificial Neural Networks", Journal of Computing in Civil Engineering, ASCE, 8(2) 179-200, 1994. doi:10.1061/(ASCE)0887-3801(1994)8:2(179)
4
J. Moody, C.J. Darken, "Fast Learning in Networks of Locally Tuned Processing Units", Neural Computation, (1), 281-294, 1989. doi:10.1162/neco.1989.1.2.281
5
F. Moses, M. Ghosn, "Instrumentation for Weighing Trucks-In-Motion for Highway Bridge Loads." Report FHWA/OH-83/001, Federal Highway Administration, McLean, Virginia, 1983.

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