Theoretical framework for modeling the long-term performance of pavement routine maintenance using Markov chain.

This paper proposes three different models for investigating the long-term performance of routine maintenance works using the discrete-time Markov model. The three models are called (M1, M2, and M3), which represent specific forms of the transition probability matrix. The (M1) model only incorporates the routine maintenance variables (M_i,i-1) under the assumption of only one-state upgrade. The (M2 and M3) models incorporate both routine maintenance variables (M_i,i-1), and major rehabilitation variables (Q_i,1) under the assumption of upgrade to condition state (1). Two different modeling methods are proposed for pavement long-term performance prediction in the presence of maintenance and rehabilitation (M&R) variables. The first one is the project-level approach as applied to small pavement networks which assumes that all M&R works can be performed in a short-time period. The second one is the network-level approach which requires the M&R works to be spanned over the entire year as applied to large pavement networks. A model cost-effectiveness index is proposed to evaluate the long-term performance of potential M&R schedules under an unconstrained annual budget. In contrast, M&R variable cost-effectiveness indices are proposed as useful parameters for yielding optimal M&R schedules under a constrained annual budget. The sample results indicated the unrealistic performance of the (M1) model and the usefulness of the (M2 and M3) models in yielding reliable long-term M&R schedules. [ABSTRACT FROM AUTHOR]