Optimum Decision Policy for Management of Pavement Maintenance and Rehabilitation

Abstract

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.

Distress Identification, Cost Analysis and Pavement Temperature Prediction for the Long-Term Pavement Performance for Western Australia

“…However, in non-linear programming, this assumption does not accumulate at all [10]. Abaza and Ashur [11] developed their model based non-linear programming. Pavement condition prediction models are significant component of pavement optimization models.…”

“…The non-linear model for pavement maintenance and rehabilitation optimization is formulated as follows [9], [11]:…”

Developing a new road deterioration model incorporating flooding

“…Markov chains have been used for infrastructure deterioration predictions of bridges (Fu and Debraj, 2008;Ranjith et al, 2011), roads (Abaza and Ashur, 1999;Hudson et al, 1998;Lethanh and Adey, 2012;Li, 1997;Li and Haas, 1998;Madanat et al, 1995;Ortiz-Garcia et al, 2006;Panthi, 2009;Tack and Chou, 2002;Wang et al, 1994), waste water (Hyeon-shik et al, 2006), rail (Ferreira and Murray, 1997;Shafahi and Hakhamaneshi, 2009) and pipelines (Sinha and Mark, 2004). …”

Long-Term Pavement Performance Effectiveness of Preventive Maintenance Treatments Using Markov Chain Algorithm

“…The impacts of pavement deterioration and preservation strategy at the same time for the network analysis can be modeled by using Markov chain process [8,[13][14][15][16]. A pavement begins its life in nearly perfect condition (i.e., no distress) and is then subjected to a sequence of duty cycles that cause the pavement condition to deteriorate, and consequently it exhibits more distresses.…”

“…As a result, there is a need for a rational and a practical methodology for evaluating costeffectiveness and estimating optimal timings of such treatments. The latter concern have been addressed in several studies [5][6][7][8][9] both at project and network levels. In this paper, the short-and the long-term network level effectiveness of preventive maintenance treatments from the LTPP SPS-3 experiment is evaluated using Markov chain algorithm (MCA).…”