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KSCE Journal of Civil Engineering

, Volume 14, Issue 3, pp 343–351 | Cite as

Estimation of Markovian transition probabilities for pavement deterioration forecasting

  • Kiyoshi Kobayashi
  • Myungsik DoEmail author
  • Daeseok Han
Highway Engineering

Abstract

While it is impossible to estimate when a road section will collapse, the understanding of road section deterioration can help asset managers predict the condition of road sections and take appropriate actions for rehabilitations. Deterioration forecasting modeling is an essential element for an efficient pavement management system. Although the Pavement Management System (PMS) has been introduced and operated for optimal road maintenance since the late 1980s in Korea, some problems for accurate prediction of road deterioration remain due to the quality of pavement performance data and the different pavement structural, material and environmental conditions. In this paper, a methodology to estimate the Markov transition probability model is presented to forecast the deterioration process of road sections. The deterioration states of the road sections are categorized into several ranks and the deterioration processes are characterized by hazard models. The Markov transition probabilities between the deterioration states, which are defined by the non-uniform or irregular intervals between the inspection points in time, are described by the exponential hazard models. Furthermore, in order to verify the validity of the proposed method, the applicability of the estimation methodology presented in this paper is investigated by using the empirical surface data set of the national highway in Korea.

Keywords

Markov transition probability asset management exponential hazard function road pavement deterioration forecasting pavement management system 

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References

  1. Abaza, K. A., Ashur, S. A., and Al-Khatib, I. A. (2004). “Integrated pavement management system with a Markovian prediction model.” J. of Transp. Engrg., Vol. 130, No. 1, pp. 24–33.CrossRefGoogle Scholar
  2. Amemiya, T. (1985). Advanced Econometrics, Harvard University Press, Cambridge, MA.Google Scholar
  3. Butt, A. A., Shahin, M. Y., Carpenter, S. H., and Carnahan, J. V. (1994). “Application of Markov process to pavement management systems at network level.” 3rd International Conference on Managing Pavements, TRB, Vol. 2, pp. 159–172.Google Scholar
  4. Cesare, M. A., Santamarina, J. C., Turstra, C. J., and Vanmarcke, E. (1992). “Modeling bridge deterioration with Markov chains.” J. of Transp. Engrg., Vol. 118, No. 6, pp. 821–833.Google Scholar
  5. Chung, S., Hong, T., Han, S. Son, J., and Lee, S. (2006). “Life cycle cost analysis based optimal maintenance and rehabilitation for underground infrastructure management.” KSCE J. of Civil Engineering, Vol. 10, No. 4, pp. 243–253.CrossRefGoogle Scholar
  6. Federal Highway Administration (FHWA). (1993). PONTIS technical manual, Publ. No. FHWA-SA-94-031, U.S. Dept. of Transp., Washington, D.C.Google Scholar
  7. Gourieroux, C. (2000). Econometrics of qualitative dependent variables, Cambridge University Press.Google Scholar
  8. Greene, W. H. (1997). Econometric analysis, 3rd. Ed., Macmillan, New York.Google Scholar
  9. Guignier, F. and Madanat, S. (1999). “Optimization of infrastructure systems maintenance and improvement policies.” J. of Infrastructure Systems, Vol. 5, No. 4, pp. 124–134.CrossRefGoogle Scholar
  10. Jiang, M., Corotis, R. B., and Ellis, H. (2000). “Optimal life-cycle costing with partial observability.” J. of Infrastructure Systems, Vol. 6, No. 2, pp. 56–66.CrossRefGoogle Scholar
  11. Jiang, Y., Saito, M., and Sinha, K. C. (1989). “Bridge performance prediction model using the Markov chain.” Transp. Res. Rec. 1180, TRB, pp. 25–32.Google Scholar
  12. Kim, S. and Kim N. (2006). “Development of performance prediction models in flexible pavement using regression analysis method” KSCE J. of Civil Engineering, Vol. 10, No. 2, pp. 91–96.CrossRefGoogle Scholar
  13. Lancaster, T. (1990). The econometric analysis of transition data, Cambridge University Press.Google Scholar
  14. Lee, T. C., Judge, G. G., and Zellner, A. (1970). Estimating the parameters of the Markov probability model from aggregate time series data, Amsterdam, North-Holland.Google Scholar
  15. Loizos, A. and Karlaftis, M.G. (2005). “Prediction of pavement crack initiation from in-service pavements: A duration model approach” J. of the Transportation Research Board, No.1940, TRB, pp. 38–42.Google Scholar
  16. Madanat, S. M., Mishalani, R., and Wan Ibrahim, W. H. (1995). “Estimation of infrastructure transition probabilities from condition rating data.” J. of Infrastructure Systems, Vol. 1, No. 2, pp. 120–125.CrossRefGoogle Scholar
  17. Mishalani, R. G. and Madanat, S. M. (2002). “Computation of infrastructure transition probabilities using stochastic duration models.” J. of Infrastructure Systems, Vol. 8, No. 4, pp. 139–148.CrossRefGoogle Scholar
  18. Park, S. (2004). “Identifying the hazard characteristics of pipes in water distribution systems by using the proportional hazard model: 2. Applications.” KSCE J. of Civil Engineering, Vol. 8, No. 6, pp. 669–677.CrossRefGoogle Scholar
  19. Prozzi, J. A. and Madanat, S. M. (2004). “Development of pavement performance models by combining experimental and field data.” J. of Infrastructure Systems, Vol. 10, No. 1, pp. 9–22.CrossRefGoogle Scholar
  20. Shin, H. (2006). “Development of a semi-parametric stochastic model of asphalt pavement crack initiation.” KSCE J. of Civil Engineering, Vol. 10, No. 3, pp. 189–194.CrossRefGoogle Scholar
  21. Tobin, J. (1958). “Estimation of relationships for limited dependent variables.” Econometrica, Vol. 26, pp. 24–36.zbMATHCrossRefMathSciNetGoogle Scholar
  22. Tsuda, Y., Kaito, K., Aoki, K., and Kobayashi, K. (2005) “Estimating markovian transition probabilities for bridge deterioration forecasting.” JSCE Journal, No.801/IV-73, pp. 69–82 (in Japanese).Google Scholar
  23. Wee, S. and Kim, N. (2006). “Angular fuzzy logic application for pavement maintenance and rehabilitation strategy in Ohio.” KSCE J. of Civil Engineering, Vol. 10, No. 2, pp. 81–89.CrossRefGoogle Scholar
  24. Yang, J., Gunaratne, M., Lu, J. J., and Dietrich, B. (2005). “Use of recurrent Markov chains for modeling the crack performance of flexible pavements.” J. of Transportation Engineering, Vol. 131, No. 11, pp. 861–872.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Dept. of Urban ManagementKyoto UniversityKyotoJapan
  2. 2.Dept. of Urban EngineeringHanbat National UniversityDaejeonKorea

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