Performance Modeling of Kaligawe Road in Semarang Using Markov Chains

Abstract

here are two kinds of pavement performance modeling, deterministic and stochastic. Among the stochastic modeling, Markov Chains receives a considerable attention ( PerezAcebo et al. 2017 ). Modeling pavement performance using Markov Chains were about developing Transition Probability Matrix (TPM) and present state vector. A model then can be developed by multiplying these two factors. This paper aimed to model pavement performance of a rigid pavement road. The object was Kaligawe road. Kaligawe road is in the northern part of the city of Semarang. It is a 6 km long and 15 meter wide road, divided into two lanes. There were two pavement performance models in this paper; the first one compared the real IRI data and the predicted one. The second model predicted IRI values using July’17 IRI data for the next two cycle times. The first model suggested a new IRI data should be used if there was a Maintenance and Rehabilitation work (M&R work) before. The second model showed that the accuracy of the prediction was not reach 100%, it can be seen from the gap between the real total number of no M&R work section and the predicted one. Keywords :PavementPerformanceModeling; RigidPavement; MarkovChains