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- W566708883 abstract "Timely identification of undesirable crack, ride and rut conditions has been a critical step in pavement management at the network level. To date, many models have been developed for forecasting of pavement conditions with most of them focusing on a single index. Florida Department of Transportation (FDOT) forecasting models for roughness, skid resistance, or crack condition, etc., are such examples. The overall pavement surface condition is jointly determined by these individual pavement condition indices. This report summarizes the results of a research project that was initiated to implement a pavement condition prediction methodology using the Artificial Neural Network (ANN). In this research effort, three individual ANN models were developed to forecast three key indices, including crack rating, ride rating, and rut rating. These indices have been used by the FDOT for pavement evaluation purposes. Each individual model was trained and tested with the use of the FDOT pavement surface condition database. Modeling results suggest that the ANN models developed in the research have the capability to satisfactorily forecast future individual pavement condition index up to a period of five years. As one of the implementation tasks, a software package was developed for easy implementation and use of the ANN predictive models." @default.
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- W566708883 date "2003-03-01" @default.
- W566708883 modified "2023-09-22" @default.
- W566708883 title "APPLICATION OF NEURAL NETWORK MODELS FOR FORECASTING OF PAVEMENT CRACK INDEX AND PAVEMENT CONDITION RATING" @default.
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