Use of neural networks for condition rating of jointed concrete pavements

Abstract

This paper discusses the use of backpropagation neural networks as a management tool for the maintenance of jointed concrete pavement. The backpropagation algorithm is applied to model the condition rating scheme adopted by Oregon State Department of Transportation. The backpropagation technique was successful in accurately capturing the nonlinear characteristics of the condition rating model. A large training set of actual pavement condition cases was used to train the network. The training was terminated when the average training error reached 0.022. A set of 6802 cases was used to test the generalization ability of the system. The trained network was able to accurately determine the correct condition ratings with an average testing error of 0.024. Finally, a statistical hypothesis test was conducted to demonstrate the system's fault-tolerance and generalization properties.