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KSCE Journal of Civil Engineering

, Volume 18, Issue 7, pp 2107–2119 | Cite as

Application of Bayesian estimation method with Markov hazard model to improve deterioration forecasts for infrastructure asset management

  • Daeseok HanEmail author
  • Kiyoyuki Kaito
  • Kiyoshi Kobayashi
Highway Engineering

Abstract

The heart of asset management systems for road infrastructure is the deterioration forecasting model. It provides the most fundamental information for better asset management. So far, there are many practices to build a reliable forecasting model using inspection data in conjunction with statistical theories. In many applications, however, an inadequate stock of inspection data or difficulty in applying sophisticated statistical methods have often been serious obstacles. As a solution, this paper suggests applying the Bayesian estimation method with a multi-state exponential hazard Markov chain model for simple and reliable deterioration forecasting for infrastructure. The main contents of this paper are an introduction of the model’s framework combining Markov chain, hazard theory, and Bayesian estimation method, and a demonstration of its practical application with an empirical study. The empirical study was conducted with time-series inspection data of pavement from the Korean National Highways. The estimation results from the suggested method would be useful for improving the current pavement maintenance strategy for Korean National Highways. However, the most important message of this paper is that the framework of the Bayesian Markov hazard model could be the best model to use for other civil infrastructure that has gradual changes in condition. The great advantages from the Bayesian estimation method may facilitate development of customized asset management systems.

Keywords

asset management pavement deterioration forecasting Bayesian estimation method Markov chain Monte Carlo simulation Bayesian Markov hazard model Korean national highways 

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References

  1. Chib, S. (1995). “Marginal likelihood from Gibbs output.” J. of the American Statistical Association, Vol. 90, pp. 1313–1321, DOI:  10.1080/01621459.1995.10476635.MathSciNetCrossRefzbMATHGoogle Scholar
  2. Gelman, A. (1992). “Iterative and non-iterative simulation algorithms.” Computing Science and Statistics 24 (Interface Proceedings), pp. 433–438, DOI: 10.1.1.63.7666.Google Scholar
  3. Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments, in Bernardo, J. M., Berger, J.M., Dawid, A.P. and Smith A.F.M (Eds.), Bayesian Statistics, Oxford University Press, New York, pp. 169–193, DOI: 10.1.1.27.2952.Google Scholar
  4. Han, D. (2011). Development of open-source hybrid pavement management system for an international standard, PhD Thesis, Kyoto University, Japan.Google Scholar
  5. Han, D., Do, M., Kim, S., and Kim, J. (2007). “Life cycle cost analysis of pavement maintenance standard considering user and socioenvironmental cost.” J. of the Korean Society of Civil Engineers, KSCE, Vol. 27, No. 6d, pp. 727–740 (in Korean).Google Scholar
  6. Hastings, W. K. (1970). “Monte Carlo sampling methods using Markov chains and their applications.” Biometrika, Vol. 57, No. 1, pp. 97–109, DOI:  10.1093/biomet/57.1.97.CrossRefzbMATHGoogle Scholar
  7. Ibrahim, J., Ming-hui, C., and Sinha, D. (2001). Bayesian survival analysis, Springer Series in Statistics.CrossRefzbMATHGoogle Scholar
  8. Jido, M., Otazawa, T., and Kobayashi, K. (2008). “Optimal repair and inspection rules under uncertainty.” J. of Infrastructure Systems, ASCE, Vol. 14, No. 2, pp. 150–158, DOI:  10.1061/(ASCE)1076-0342(2008)14:2(150).CrossRefGoogle Scholar
  9. Kaito, K. and Kobayashi, K. (2007). “Bayesian estimation of Markov deterioration hazard model.” JSCE J. of Civil Engineering, Vol. 63, No. 2, pp. 336–355, DOI:  10.2208/jsceja.63.336 (in Japanese).Google Scholar
  10. Kaito, K., Yasuda, K., Kobayashi, K., and Owada, K. (2005). “Optimal maintenance strategies of bridge components with an average cost minimizing principles.” JSCE J. of Earthquake Engineering, Nos. I-73/801, pp. 83–96, DOI:  10.2208/jscej.2005.801_83.Google Scholar
  11. Kobayashi, K. and Kuhn, K. (2007). The management and measurement of infrastructure: Performance, efficiency and innovation, New Horizons in Regional Science.Google Scholar
  12. Kobayashi, K., Do, M., and Han, D. (2010a). “Estimation of Markovian transition probabilities for pavement deterioration forecasting.” KSCE J. Civil. Eng., KSCE, Vol. 14, No. 3, pp. 341–351, DOI:  10.1007/s12205-010-0343-x.CrossRefGoogle Scholar
  13. Kobayashi, K., Kaito, K., and Nam, L.T. (2010b). “Deterioration forecasting model with multistage weibull hazard functions.” J. of Infrastructure Systems, ASCE, Vol. 16, No. 4, pp. 282–291, DOI:  10.1061/(ASCE)IS.1943-555X.0000033.CrossRefGoogle Scholar
  14. Kobayashi, K., Kaito, K., and Nam, L.T. (2011). “A statistical deterioration forecasting method using hidden Markov model for infrastructure management.” Transportation Research Part B, Vol. 46, No. 4, pp. 544–561, DOI:  10.1016/j.trb.2011.11.008.CrossRefGoogle Scholar
  15. Koop, G., Poirier, D. J., and Tobias J. L. (2007). Bayesian econometric methods, Cambridge University Press, New York.CrossRefGoogle Scholar
  16. Lancaster, T. (1990). The econometric analysis of transition data, Cambridge University Press, New York.zbMATHGoogle Scholar
  17. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A., and Teller, H. (1953). “Equations of state calculations by fast computing machines.” J. of Chemical Physics, Vol. 21, No. 6, pp. 1087–1091, DOI:  10.1063/1.1699114.CrossRefGoogle Scholar
  18. Nam, L.T. (2009). Stochastic optimization methods for infrastructure management with incomplete monitoring data, PhD Thesis, Kyoto University, Japan.Google Scholar
  19. Newey, W. W. and West, K. D. (1987). “A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix.” Econometrica, Vol. 55, No. 3, pp. 703–708, DOI:  10.2307/1913610.MathSciNetCrossRefzbMATHGoogle Scholar
  20. Permanent International Association of Road Congress (PIARC) (2000). Highway development and management series, Vol. 1~7, The World Road Association, 92055 La Defense, France.Google Scholar
  21. Sugisaki, T., Kaito, K., and Kobayashi, K. (2006). “Statistical deterioration prediction considering nonuniformity of visual inspection cycle.” JSCE J. of Structural Engineering, No. 52A(2), pp. 781–790.Google Scholar
  22. Train, K. E. (2009). Discrete choice methods with simulation (second edition), Cambridge University Press, New York, USA.CrossRefzbMATHGoogle Scholar
  23. Tsuda, Y., Kaito, K., Aoki, K., and Kobayashi, K. (2006a). “Estimating Markovian transition probabilities for bridge deterioration forecasting.” JSCE J. of Structural Engineering / Earthquake Engineering, Vol. 23, No. 2, pp. 241s–256s, DOI:  10.1007/s12205-010-0343-x.CrossRefGoogle Scholar
  24. Tsuda, Y., Kaito, K., Yamamoto, H., and Kobayashi. (2006b). “Bayesian estimation of weibull hazard models for deterioration forecasting.” JSCE J. of Construction Engineering and Management, Vol. 62, No. 3, pp. 473–491, DOI:  10.2208/jscejf.62.473.CrossRefGoogle Scholar
  25. Walubita, L. F., Liu, W., and Scullion, T. (2010). The Texas perpetual pavements: Experience overview and the way forward, Technical Report (FHWA/TX-10/0-4822-3), Texas Department of Transportation, Austin, Texas.Google Scholar
  26. Walubita, L. F., Das, G., Espinoza, E., Oh, J., Scullion, T., Lee, S., Garibay, J. L., Nazarian, S., and Abdallah, I. (2012). Texas flexible pavements and overlays: Year 1 report — test sections, data collection, analyses, and data storage system, Technical Report (FHWA/TX-12/0-6658-1), Texas Department of Transportation, Austin, Texas.Google Scholar

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Daeseok Han
    • 1
    Email author
  • Kiyoyuki Kaito
    • 2
  • Kiyoshi Kobayashi
    • 3
  1. 1.Highway Pavement Research DivisionKorea Institute of Civil and Building TechnologyGoyangKorea
  2. 2.Dept. of Civil EngineeringOsaka UniversityOsakaJapan
  3. 3.Dept. of Urban ManagementKyoto UniversityKyotoJapan

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