00966563

Component

https://doi.org/10.3141/1853-01

http://scholar.google.com...iang&publication_year=2003

Timely identification of undesirable crack, ride, and rut conditions is a critical issue in pavement management systems at the network level. The overall pavement surface condition is determined by these individual pavement surface conditions. A research project was carried out to implement an overall methodology for pavement condition prediction that uses artificial neural networks (ANNs). In the research, three ANN models were developed to predict the three key indices--crack rating, ride rating, and rut rating--used by the Florida Department of Transportation (FDOT) for pavement evaluation. The ANN models for each index were trained and tested by using the FDOT pavement condition database. In addition to the three key indices, FDOT uses a composite index called pavement condition rating (PCR), which is the minimum of the three key indices, to summarize overall pavement surface condition for pavement management. PCR is forecast with a combination of the three ANN models. Results of the research suggest that the ANN models are more accurate than the traditional regression models. These ANN models can be expected to have a significant effect on FDOT's pavement management system.

This paper appears in Transportation Research Record No. 1853, Pavement Management and Rigid and Flexible Pavement Design 2003.

English

p. 3-12

2003

Transportation Research Record

0309085890

Figures (8) ; References (8) ; Tables (3)

Accuracy; Forecasting; Neural networks; Pavement cracking; Pavement management systems; Pavement performance; Ride quality; Ruts (Pavements)

Pavement condition rating; Surface condition

Florida

Design; Highways; Pavements; I22: Design of Pavements, Railways and Guideways; I23: Properties of Road Surfaces

TRIS, TRB, ATRI

Dec 10 2003 12:00AM

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