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.pavement management information system, Bayesian adjustment, state-space models, Kalman filtering, Markov chain Monte Carlo,
Many agencies responsible for managing pavements have adopted pavement management systems (PMS) to help manage their pavement networks more cost-effectively. One of the most costly parts of operating a PMS is collecting condition information, especially pavement distress information. Many agencies have started to contract for pavement distress data collection. Some of the agencies have experienced problems with the data collected by contract. A study for agencies in Washington and Oregon to define the accuracy of data needed by the agencies with an evaluation of certain participating vendors using semiautomated data collection methods is described. Issues about quality control and quality assurance faced by agencies considering contracting for automated data collection also are raised. These issues need additional study to develop appropriate guidelines. The initial set provided is based on discussions with some of the agencies currently contracting for pavement distress data collection.
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