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 proce ss of human beings and do not require users to predefine a mathematica l equation relating pavement conditions to priority ratings, they offe r an attractive means by which the priority setting process by highway maintenance personnel can be simulated. In the present study, the abi lity of a simple back-propagation neural network was tested separately with three different priority-setting schemes, using a general-purpos e microcomputer-based neural network software. The priority-setting sc hemes 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 wa s also introduced to examine how it would affect the performance of th e neural network. Test results are positive and indicative of the pote ntial of neural networks as a useful tool that highway agencies can us e for priority rating in maintenance planning at the network level.