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Title	COMPUTING TRUCK ATTRIBUTES WITH ARTIFICIAL NEURAL NETWORKS
Accession No	00646339
Authors	Gagarin, N; Flood, I; Albrecht, P
Journal Title	Journal of Computing in Civil Engineering Vol. 8 No. 2
Corp. Authors / Publisher	American Society of Civil Engineers ; American Society of Civil Engineers
Publication Date	19940400
Description	p. 179-200; Appendices(2); Figures; References; Tables
Languages	English
Abstract	A study is described which applied neural networks to the problem of determining truck attributes (velocity, axle spacing, axle loads) purely from strain-response readings taken from the structure over which the truck is traveling. The study showed that the attributes of trucks in motion can be estimated within an acceptable degree of accuracy using radial Gaussian incremental learning networks solely from measured strain patterns. The approach described in the paper is a 2-layered modular network structure. The artificial neural networks in the first layer classifies the trucks, and the second-layer artificial neural networks compute estimates of the truck's velocity, axle spacings, and axle loads.
TRT Terms	Accuracy ; Axle loads ; Axles ; Bridges ; Neural networks ; Spacing ; Trucks ; Vehicle characteristics ; Velocity
Other Terms	Artificial neural networks; Axle spacings
Subject Areas	H53 VEHICLE CHARACTERISTICS; I91 Vehicle Design and Safety
Availability	American Society of Civil Engineers Find a library where document is available Order Document: http://worldcat.org/issn/08873801
TRIS Files	HRIS
Database	TRIS Online

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