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A statistical deterioration forecasting method using hidden Markov model for infrastructure management

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  • Kobayashi, Kiyoshi
  • Kaito, Kiyoyuki
  • Lethanh, Nam

Abstract

The application of Markov models as deterioration-forecasting tools has been widely documented in the practice of infrastructure management. The Markov chain models employ monitoring data from visual inspection activities over a period of time in order to predict the deterioration progress of infrastructure systems. Monitoring data play a vital part in the managerial framework of infrastructure management. As a matter of course, the accuracy of deterioration prediction and life cycle cost analysis largely depends on the soundness of monitoring data. However, in reality, monitoring data often contain measurement errors and selection biases, which tend to weaken the correctness of estimation results. In this paper, the authors present a hidden Markov model to tackle selection biases in monitoring data. Selection biases are assumed as random variables. Bayesian estimation and Markov Chain Monte Carlo simulation are employed as techniques in tackling the posterior probability distribution, the random generation of condition states, and the model’s parameters. An empirical application to the Japanese national road system is presented to demonstrate the applicability of the model. Estimation results highlight the fact that the properties of the Markov transition matrix have greatly improved in comparison with the properties obtained from applying the conventional multi-stage exponential Markov model.

Suggested Citation

  • Kobayashi, Kiyoshi & Kaito, Kiyoyuki & Lethanh, Nam, 2012. "A statistical deterioration forecasting method using hidden Markov model for infrastructure management," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 544-561.
  • Handle: RePEc:eee:transb:v:46:y:2012:i:4:p:544-561
    DOI: 10.1016/j.trb.2011.11.008
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    2. Li, Sirui & Liu, Ying & Wang, Pengfei & Liu, Peng & Meng, Jun, 2020. "A novel approach for predicting urban pavement damage based on facility information: A case study of Beijing, China," Transport Policy, Elsevier, vol. 91(C), pages 26-37.
    3. Daeseok Han & Jin-Hyuk Lee & Ki-Tae Park, 2022. "Deterioration Models for Bridge Pavement Materials for a Life Cycle Cost Analysis," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    4. Assaf, A. George & Gillen, David & Tsionas, Efthymios G., 2014. "Understanding relative efficiency among airports: A general dynamic model for distinguishing technical and allocative efficiency," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 18-34.
    5. Wenfei Bai & Quanxin Sun & Futian Wang & Rengkui Liu & Ru An, 2019. "A segmental evaluation model for determining residual rail service life based on a discrete-state conditional probabilistic method," Journal of Risk and Reliability, , vol. 233(2), pages 211-225, April.
    6. Kobayashi, K. & Kaito, K. & Lethanh, N., 2014. "A competing Markov model for cracking prediction on civil structures," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 345-362.
    7. Xiong, Yingge & Tobias, Justin L. & Mannering, Fred L., 2014. "The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 109-128.

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