Predicting the deterioration of the forest road network by employing the Markov chain (Case study: Forest road in the western part of Haraz)

Abstract

Forest road networks play an important role to achieve management goals in forest ecosystems and despite of implementation of the forest logging ban, their protection and maintenance cannot be stopped. Therefore, in order to allocate budget and prioritize the maintenance planning, designing a reliable model for predicting the condition of road pavement failure has great importance. The purpose of this study is to predict the performance of road pavement for planning its maintenance management using Markov chain theory. In the present study, 185 road sections in the forest roads of West Haraz were selected and the data of pavement, surface and roadside conditions were measured in different plots during 2015-2018. Then, the functional quality of the road was evaluated by the PCI index and the developed index for forest roads (FRPCI). The pavement quality was classified into five classes: excellent, good, average, bad and broken. After assessing the current situation, the road future situation was predicted over a period of ten years using the Markov chain. The results of this study based on the Markov chain model showed that the process of road decline from the upper class to the good is faster than the process of changing it from the middle class to the poor. Given that a large amount of capital is allocated in forest management plans to road construction and maintenance, need to speed up the protection and maintenance of the road, especially on better quality roads is vital. According to the study, the Markov chain model has sufficient capability in predicting the process of road deterioration and road network maintenance programs.

Keywords

• decision support system
• road break modeling
• road maintenance
• road pavement management.