599
Views
53
CrossRef citations to date
0
Altmetric
Original Articles

Stochastic Modeling of Pavement Performance

Pages 235-243 | Received 18 Jul 2002, Published online: 31 Jan 2007
 

Abstract

Pavement performance deterioration cannot be predicted precisely because traffic and environmental actions as well as the material properties and geometric variables of pavement systems are uncertain. Therefore, the prediction of the pavement performance should be carried out based on a probabilistic framework. A simple probabilistic approach is developed in this study for predicting pavement performance. The approach is based on a nonhomogenous continuous Markov chain. Its use in conjunction with the flexible pavement deterioration models in the Ontario Pavement Analysis of Cost (OPAC) and in the AASHTO guide is explored. The proposed approach is more efficient than the ones found in the literature since the probability transition matrix in this study depends only on two model parameters, one controlling the intensity of transition and the other controlling the time transformation. The proposed approach seems able to mimic well the pavement degradation process predicted by the OPAC and AASHTO models.

Acknowledgements

The financial support of the Natural Science and Engineering Research Council of Canada is gratefully acknowledged. We thank the anonymous reviewers for their constructive comments.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 225.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.