A Bayesian approach for improved pavement performance prediction

Eun Sug Park, Roger Smith, Thomas Freeman and Clifford Spiegelman

Journal of Applied Statistics, 2008, vol. 35, issue 11, 1219-1238

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.

Keywords: pavement management information system; Bayesian adjustment; state-space models; Kalman filtering; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (1)

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DOI: 10.1080/02664760802318651

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