Dynamic Probabilistic Approach for Long-Term Pavement Restoration Program with Added User Cost

Abstract

A dynamic probabilistic-based approach has been developed for generating a long-term pavement restoration program for a given pavement system. The probabilistic approach applies the basic principles of stochastic processes to predict pavement conditions. Initial state probabilities and transition probabilities are the two main parameters required to develop the future state probability functions used in formulating an effective optimum decision policy. The future state probabilities are only functions of the restoration variables representing potential restoration actions. An optimum decision policy is deployed to yield a pavement restoration program comprising (n) restoration plans corresponding to (n) transitions. The derived pavement restoration program takes into consideration the long-term transitional performance and budget requirements. The applied decision policy is based on either maximizing the expected system condition ratings subjected to budget constraints or minimizing the net system costs subjected to desired expected system condition ratings. The net system cost may include restoration cost, deteriorated pavement added user cost, and work-zone added user cost. The two decision policy options are subjected to other constraints placing limits on the state probability functions and restoration variables. The restoration variables as applied to state probability functions result in optimum models that are sequentially solved to maintain their linearity. Sample results from a case study have indicated the usefulness of the developed dynamic probabilistic approach in yielding potential long-term pavement restoration programs.

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