Neural networks and AASHO road test

M. R. Banan, Keith Hjelmstad

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

The American Association of State Highway Officials (AASHO) road test, conducted during the period of 1958 through 1960, was a factorial test of pavement durability that considered layer depths, axle load, and number of load applications as the primary variables. These data were processed using traditional statistical techniques. The AASHO formula is the resulting databased model of the road-test data. In the present paper, we reexamine the AASHO road-test data, using the Monte Carlo Hierarchical Adaptive Random Partitioning (MC-HARP) neural-network model developed by Banan and Hjelmstad (1995), and show that an MC-HARP model can represent the data far better than the AASHO formula can. We conclude that the MC-HARP neural network may be an appropriate tool for the development of databased models of pavement performance in the future.

Original languageEnglish (US)
Pages (from-to)358-366
Number of pages9
JournalJournal of Transportation Engineering
Volume122
Issue number5
StatePublished - Sep 1996
Externally publishedYes

Fingerprint

neural network
road
Neural networks
Pavements
Axles
Loads (forces)
Durability
performance

ASJC Scopus subject areas

  • Civil and Structural Engineering

Cite this

Neural networks and AASHO road test. / Banan, M. R.; Hjelmstad, Keith.

In: Journal of Transportation Engineering, Vol. 122, No. 5, 09.1996, p. 358-366.

Research output: Contribution to journalArticle

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