An integrated pavement management system has been designed to provide the pavement engineers with an effective decisionmaking tool for planning and scheduling of pavement maintenance and rehabilitation ͑M&R͒ work. The developed system applies a discrete-time Markovian model to predict pavement deterioration with the inclusion of pavement improvement resulting from M&R actions. An effective decision policy with two major options has been used. The first option optimizes a generalized nonlinear objective function that is defined in terms of proportions of pavement sections in the five deployed condition states, and is subjected to budget constraints. The second option minimizes M&R cost which is subjected to preset pavement condition requirements in terms of state proportions at the end of a selected study period. The system applies two approaches for the selection of pavement project candidates. The first approach is based on random selection of pavement sections within the same condition state, while the second one relies on worst-first selection within the same condition state. The optimization process is performed using two different optimization methods which are the penalty function method and uniform search method.

An effective practical decision policy has been developed for use in the selection of an optimum maintenance and rehabilitation program. Its main objective is the optimization of pavement condition under constrained budgets. The developed policy utilizes a discrete-time Markovian model with five condition states labeled a, b, c, d, and f. State a represents pavements in excellent condition, and State f indicates pavements in bad condition. Several decision options have been introduced based on either maximizing the proportion of “good” pavements or minimizing the proportion of “bad” pavements. State probabilities at some desired future time have been used as the main objective functions in the development of optimum maintenance and rehabilitation programs. The unknown variables in these programs are those representing improvements to pavement condition through implementation of maintenance and rehabilitation work. The resulting optimum programs are nonlinear in form, and therefore the penalty function method with functional evaluations has been successfully used to yield optimum solutions. The optimum solution to a particular program defines the type and extent of maintenance and rehabilitation work required for annual or biennial implementation. Pavement maintenance is mainly defined as routine maintenance consisting of filling cracks, patching potholes, and other applicable techniques such as chip seal coat or slurry seal. Pavement rehabilitation is defined as major rehabilitation actions to include resurfacing (overlay), resurfacing with partial reconstruction (localized reconstruction), and complete reconstruction applied to pavements in States c, d, and f, respectively.

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This paper presents a new technique to estimate the transition probabilities used in the Markovian-based pavement performance prediction models. The proposed technique is based on the 'back-calculation' of the discrete-time Markov model using only two consecutive cycles of pavement distress assessment. The transition probabilities, representing the pavement deterioration rates, are the main elements of the Markov model used in predicting future pavement conditions. The paper also presents a simplified procedure for evaluating the pavement state of distress using the two major pavement defect groups, namely cracking and deformation. These two defect groups are to be identified and evaluated for pavement sections using visual inspection and simple linear measurements. The extent of these two major defect groups is measured using the defected pavement areas (or lengths) and the defect severity is measured based on the average crack width and average deformation depth. A case study is presented to demonstrate the 'back-calculation' of transition probabilities. In particular, the impacts of the pavement section length on the distress rating and on the estimation of the transition probabilities have been investigated. The results have indicated that the estimated transition probabilities become highly unstable as the section length gets larger and the sample size becomes smaller.

A flexible pavement life-cycle model has been developed to yield an optimum maintenance and rehabilitation plan. The model incorporates into the optimization process both performance and cost associated with a life-cycle analysis period for a given pavement structure ͑project͒. A single life-cycle indicator called ''life-cycle disutility'' has been introduced and defined as the ratio of cost to performance. The optimum plan is the one associated with the minimum life-cycle disutility value. The model evaluates several potential maintenance and rehabilitaton plans generated according to two defined decision policy options. The first decision policy option requires a fixed analysis period, whereas the second one involves a variable analysis period. Both options require a specified number of major rehabilitation cycles. Pavement life-cycle cost includes initial construction, scheduled major rehabilitation cycles, and routine maintenance and added user cost. Pavement life-cycle performance is defined as the area under the life-cycle performance curve either generated from actual pavement distress data or based on an incremental analysis of the American Association of State Highway and Transportation Officials basic design equation of flexible pavement.