Application Of Markovian Probabilistic Process To Develop A Decision Support System For Pavement Maintenance Management

Abstract Pavement deterioration rate is very difficult to predict because of the complexities in determining the pavement condition rating or difficulty in collecting detailed data particularly in absence of sophisticated equipment or trained staff etc. A wide variety of Pavement Maintenance Systems PMS are used but unfortunately either these systems do not use a formalized procedure to determine the pavement condition rating or they assign the pavement state transition probabilities on the basis of experience. The object of this paper is to apply the Markovian probability process of operational research to develop a decision support system DSS to predict the future condition of the pavement. Significance of the collected sample data of 20 pavement sections is first tested for Markovian properties. 26935-inference test is used to check the goodness of fit. Poissons method is used for calculating successive transition matrices for predicting future condition state of pavements. The results support the Markovian probabilistic process tool for finding the future condition states of pavements at any particular year. Improvement in condition of pavement after repair can be easily compared. It will help find the optimal maintenance and repair policy w.r.t. budgetary limits and current state of pavement condition.