ABSTRACT

A key aspect of pavement management systems is the need for reliable pavement condition data because this data are used to assess pavement network condition, to schedule Maintenance and Rehabilitation (M&R), and to estimate the level of funding needed for M&R. Poor quality of data leads to misclassification of the pavement condition, mistiming of M&R investments, inaccurate reporting of the effectiveness of individual projects or systemwide programs, and ultimately, wasteful spending of agency budgets or user frustration due to unduly deferred maintenance. Reporting pavement condition for a wide variety distress indicators across the entire carriageway and over several miles means that massive amount of data need to be collected. This study developed Gaussian-distribution regression models that captured the relationship between pavement indicators. The developed models could use a single indicator to predict the occurrence or severity of other indicators, which could help reduce data collection and processing efforts.