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A Bayesian approach for improved pavement performance prediction

Author

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  • Eun Sug Park
  • Roger Smith
  • Thomas Freeman
  • Clifford Spiegelman

Abstract

We present a method for predicting future pavement distresses such as longitudinal cracking. These predicted distress values are used to plan road repairs. Large inherent variability in measured cracking and an extremely small number of observations are the nature of the pavement cracking data, which calls for a parametric Bayesian approach. We model theoretical pavement distress with a sigmoidal equation with coefficients based on prior engineering knowledge. We show that a Bayesian formulation akin to Kalman filtering gives sensible predictions and provides defendable uncertainty statements for predictions. The method is demonstrated on data collected by the Texas Transportation Institute at several sites in Texas. The predictions behave in a reasonable and statistically valid manner.

Suggested Citation

  • Eun Sug Park & Roger Smith & Thomas Freeman & Clifford Spiegelman, 2008. "A Bayesian approach for improved pavement performance prediction," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(11), pages 1219-1238.
    • Handle: RePEc:taf:japsta:v:35:y:2008:i:11:p:1219-1238
    DOI: 10.1080/02664760802318651
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    Cited by:

    1. 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.

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