Pavement Surface Condition Prediction by Markov Chains

A model for prediction of pavement surface condition plays a vital role in Pavement Management System (PMS). In developing countries like India, PMS is the needed approach for the optimum utilisation of the available scarce resources. Pavement management System is concerned with optimal use of materials in time and space, leading to cost optimization. This Paper focuses on methodology involved in the prediction of pavement condition using probabilistic techniques. As pavement condition prediction parameter like traffic loading, pavement materials, construction methods and environmental condition are not deterministic therefore probabilistic techniques is used i.e. the present study. Markov chains have the property that probabilities involving the process will evolve in the future depend only on the present state of the process and so are independent of the events in the past. The state of the transition matrix will be defined based on the Pavement Condition Index (PCI) and element of the transition matrix will be determined by the probability method. Fifteen cycles was carried out because the expected design life of the bituminous roads is fifteen years. The probabilistic model requires only a minimal amount of data such as pavement class, pavement condition of two consecutive years and the pavement length. PCI shall be utilized as index and the present technique in pavement management systems will create good systems which may lead to more savings of the roads maintenance funds and enhance the ability of the roads network to provide better level of service.

  • Supplemental Notes:
    • Abstract reprinted with permission from the publisher.
  • Authors:
    • Suman, S K
    • Sinha, S
  • Publication Date: 2018


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: pp 50-54
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01685569
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Nov 13 2018 10:21AM