Modeling the condition of the pavement using Markov Chain method on Piru-Kairatu road segment

The purpose of this study is to predict changes in road pavement conditions in the future for a period of five years by applying the Markov chain model, based on maintenance action against pavement damage every year. The modeling used in this research is the Markov chain method with pavement damage parameter variable using IRI. The pavement data is on the Piru-Kairatu road segment in 2020-2021. These data are used to determine the initial vector (a0) and the Probability Matrix (TPM) of Maintenance. The multiplication between a0 and TPM produces Prediction of Future Pavement Conditions. The Markov chain model on the Piru-Kairatu road network for five years using the Transition Probability Matrix in 2020-2021, assuming the handling of damage to the entire road network is carried out according to the level of damage each year without delay, resulting in an increase in road conditions in every year. Where Good condition (1) increases yearly, from 37.25% to 70.72% in 5th year. Fair condition (2) decreases from 56% to 28.77% in the 5th year. Poor condition (3) decreases from 6.5% to 0.51% in 5th year. Poor conditions (4) also decreased from 0.3% to 0% in 5th years.