Optimum Stochastic Model for Pavement Management (05-0136)
Khaled A. Abaza, Birzeit University, Palestine

A pavement management model is proposed that deploys a non-homogenous discrete Markov chain for predicting the future pavement conditions for a given pavement system. A non-homogenous transition matrix is constructed to incorporate both the pavement deterioration rates and improvement rates. The pavement deterioration rates are simply the transition probabilities associated with the various deployed condition states. The improvement rates are mainly the maintenance and rehabilitation variables representing the deployed maintenance and rehabilitation actions. A decision policy is formulated based on either maximizing the expected pavement condition rating subjected to budget constraints or minimizing the maintenance and rehabilitation cost subjected to specified pavement condition ratings considering a given analysis period. The non-homogenous Markov chain allows for a variable maintenance and rehabilitation plan (matrix) for each time interval within the specified analysis period. However, the total number of maintenance and rehabilitation variables will substantially increase depending on the length of the deployed analysis period. The resulting optimum model is associated with a non-linearity order that is equal to the number of time intervals within the specified analysis period. Solving a non-linear model with a large number of variables is a very complex task. Therefore, instead of solving a single non-linear problem, a series of linear problems are formulated and iteratively solved wherein the optimal solution for one problem becomes the input for the next one. The sample results obtained from the iterative linear approach indicate the effectiveness of the proposed pavement management model in yielding optimum pavement conditions.