Neural Network Models Predict Raveling and Analyse Material/Construction Properties

M. Miradi (The Netherlands)


Artificial Neural Network (ANN), Raveling, Porousasphalt top layers, Prediction, Analysis, models


The most unacceptable damage on porous asphalt is raveling. Therefore it is important to predict when porous asphalt will achieve a critical level of raveling. In this paper Artificial Neural Network (ANN) was employed to predict raveling having input parameters related to time series data of raveling, climate, construction and traffic factors obtained from SHRP-NL database. For raveling low, Moderate and High correlation factors were R2 =0.986, R2 =0.926 and R2 =0.976. Another ANN model provided sensitivity analysis indicating relative contribution percentage of input parameters. Finally another model analyzed the relationship between materials and raveling. ANN proved to be powerful technique to predict and analyze raveling.

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