Empirical Markovian-based models for rehabilitated pavement performance used in a life cycle analysis approach

Abstract

Two empirical Markovian-based models are presented in this paper to predict the transition probabilities associated with rehabilitated pavement. The first model predicts the staged-homogenous transition probabilities as required by the staged-homogenous Markov model. The second model predicts the non-homogenous transition probabilities as applicable to the non-homogenous Markov model. In both the models, the deterioration transition probabilities are predicted as a function of the corresponding values associated with original pavement and two adjustment factors reflecting the impacts of increased traffic load applications and decreased pavement strength. The predicted transition probabilities are used to estimate the future distress ratings required for developing the corresponding life cycle performance curve. The life cycle performance/cost ratio is used to evaluate the cost-effectiveness of potential long-term M&R plans. The life cycle performance is defined as the area falling under the life cycle curve. The life cycle cost is estimated to include initial construction cost, routine maintenance cost, major rehabilitation cost, and added user cost due to work zone. Two proposed cost models are used in the case study for estimating routine maintenance and added user costs. The case study indicates that the proposed empirical Markovian-based models have provided reasonable estimates of the transition probabilities as reflected by the corresponding life cycle performance curves.

Keywords:

• Markovian processes
• pavement performance
• performance prediction
• life cycle analysis
• pavement maintenance
• pavement rehabilitation
• pavement management