A Pavement Condition-Rating Model Using Backpropagation Neural Networks

Abstract: This paper presents an overview of the neural-network technique as a management tool for maintenance of flexible pavement. The paper discusses the development and implementation of a neural network for the condition rating of roadway sections. The condition-rating scheme developed by Oregon State Department of Transportation was used as the basis for the development of the network presented. A training set of 744 cases was used to train the network, and a set of 1736 cases was used to test the generalization ability of the system. The network adequately learned the training examples with an average training error of 0.019 and was able to determine the correct condition ratings with an average testing error of 0.023. The network's ability to deal with noisy data also was tested. Up to 60% noise was added to the data and introduced to the network. The results showed that the network presented could identify condition rating relationships at high levels of-noise. Finally, an expert determination was compared with that produced by the network. The network was able to mimic the expert's condition ratings with an average error of 0.0354.