PRIORITY RATING OF HIGHWAY MAINTENANCE NEEDS BY NEURAL NETWORKS

The present paper illustrates the feasibility of using neural network models for priority assessment of highway pavement maintenance needs. Since neural networks are developed to mimic the decision-making process of human beings and do not require users to predefine a mathematical equation relating pavement conditions to priority ratings, they offer an attractive means by which the priority setting process by highway maintenance personnel can be simulated. In the present study, the ability of a simple back-propagation neural network was tested separately with three different priority-setting schemes, using a general-purpose microcomputer-based neural network software. The priority-setting schemes include a linear function relating priority ratings to pavement conditions, a nonlinear function, and subjective priority assessments obtained from a pavement engineer. For the first two schemes, noise was also introduced to examine how it would affect the performance of the neural network. Test results are positive and indicative of the potential of neural networks as a useful tool that highway agencies can use for priority rating in maintenance planning at the network level.