A Bayesian approach for improved pavement performance prediction

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.

authors

• Park, Eun

published proceedings

• JOURNAL OF APPLIED STATISTICS

author list (cited authors)

• Park, E. S., Smith, R. E., Freeman, T. J., & Spiegelman, C. H.

citation count

• 23

complete list of authors

• Park, Eun Sug||Smith, Roger E||Freeman, Thomas J||Spiegelman, Clifford H

publication date

• November 2008

publisher

• Taylor & Francis  Publisher

published in

• Journal of Applied Statistics  Journal

keywords

• Bayesian Adjustment
• Kalman Filtering
• Markov Chain Monte Carlo
• Pavement Management Information System
• State-space Models

Digital Object Identifier (DOI)

• 10.1080/02664760802318651

start page

• 1219

end page

• 1238

volume

• 35

issue

• 11-12

URL

• http%3A%2F%2Fdx.doi.org%2F10.1080%2F02664760802318651