Matias de Oliveira, JhenyfferKhani, AlirezaDavis, GaryMarasteanu, Mihai2022-05-022022-05-022022-02https://hdl.handle.net/11299/227175The main objectives of phase 2 of this project were to obtain relevant data to calculate the percent remaining service life interval (PRSI) and two additional metrics and to perform Markov chain analysis and dynamic programming to determine how much time and funding is required to bring the system to a stable configuration, which allows for more consistent planning. First, relevant pavement management data was obtained from MnDOT and preliminary data analyses were performed. The prediction models and optimization process currently used by MnDOT were investigated and summarized. Next, two additional metrics, Asset Sustainability Ratio and Deferred Preservation Liability, were calculated for MnDOT’s network. Then details of the estimation process of state-to-state transition probabilities to be used in the Markov chain model were presented. To allow for site-specific variation, ordinal logistic regression models were incorporated in the Markov chain model. The results were used in a dynamic programming optimization methodology to obtain baseline and optimal policies for different scenarios and a user-friendly excel spreadsheet tool was developed. Finally, a summary of the work performed followed by conclusions and recommendations was presented.enMarkov chainsRide qualityService lifeOptimizationReport