Simplified Markovian-based pavement management model for sustainable long-term rehabilitation planning

A simplified pavement management model for developing a long-term rehabilitation schedule is proposed for flexible pavement. The model deploys the discrete-time Markov model to predict the deterioration of both original and rehabilitated pavement performances. The main objective of the proposed model is to generate optimal annual rehabilitation cycles over a specified analysis period. This objective is achieved by optimising a cost-effectiveness index defined as the ratio of anticipated performance improvement and annual rehabilitation cost. It therefore seeks to find the optimal annual rehabilitation cycle that maximises pavement condition improvement and minimises rehabilitation cost. The corresponding optimum model is subject to a number of constraints that include the non-negativity constraints, upper-limit variable value constraints, and budget constraints. However, the rehabilitation variables are incorporated into the state probabilities rather than the transition matrix when predicting future deterioration of rehabilitated pavement. The optimum model can simply be solved using an exhaustive search approach as it makes use of a limited number of rehabilitation variables. Two case studies are presented to demonstrate the potential uses of the proposed model. The first one examined the relationship between variable budget levels and sustainable long-term pavement performances, whereas the second one investigated the long-term cost-effectiveness of individual rehabilitation treatments. Generally, the sample results re-emphasised the famous theme of ‘better roads at lower costs’.