Roads have formed the basic infrastructure of commerce since flints and other tools and artifacts were first exchanged along the trade routes of prehistory. Roadways are very large, in volume, in extent, and in value. They also wear out, and their useful life is directly proportional to their initial strength and inversely proportional to the number of heavy goods vehicles using them. Therefore, the increasing complexity of road transportation needs advanced techniques for effective design of pavements. This paper proposes an intelligent technique using neural networks to classify different types of road pavement structures, which is essential in estimating bearing capacities and load equivalency factors of pavements under different loadings.
V. Venayagamoorthy et al., "Neural Network Based Classification of Road Pavement Structures," Proceedings of International Conference on Intelligent Sensing and Information Processing, 2004, Institute of Electrical and Electronics Engineers (IEEE), Jan 2004.
The definitive version is available at https://doi.org/10.1109/ICISIP.2004.1287670
International Conference on Intelligent Sensing and Information Processing, 2004
Electrical and Computer Engineering
Keywords and Phrases
Backpropagation; Bearing Capacity Estimation; Civil Engineering Computing; Commerce Infrastructure; Feedforward Neural Nets; Heavy Goods Vehicles; Load Equivalency Factors; Multilayer Perceptrons; Neural Network Based Classification; Road Pavement Structures; Road Transportation; Roads; Transportation
Article - Conference proceedings
© 2004 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 2004