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Transportation asset management (TAM) relies on data to inform decisions on the maintenance or improvement of a given asset. Two of the essential data elements in the TAM process include the inventory, and the condition of an asset over time. Often, the condition is described using several factors, such as the amounts and types of cracking, and those factors are then combined to form a condition index. The index is frequently the basis for developing performance prediction models. However, when the condition of an asset is defined using a measure comprised of several metrics, the risk exists that considerable information could be lost or distorted from index-related calculations. Although the importance of the loss of information depends on the decision framework in which the asset is being managed, it does distort the relationship between the asset deterioration and the factors that drive that deterioration. This paper describes information loss using data and indexes from the U.S. National Park Service pavement network. The effect of the loss of information on the ability to develop performance prediction models is demonstrated. The aim was to present an approach for calculating information loss along with strategies for minimizing that loss. The results showed how loss of information can affect the ability to develop performance prediction models. The primary conclusion of this study is that information loss and -distortion should be evaluated for all condition indexes in an effort to minimize that loss and distortion.

The public has high expectations of transportation infrastructure. Namely, pavements and bridges should be in good repair and provide consistent, high-quality service. To meet these goals, the State and local [...]

The condition of a country’s roadway infrastructure significantly affects its economic development. Highway agencies that manage pavements often communicate their road network condition by using single or multiple composite condition indices. Each agency relies on its condition index to make maintenance decisions and perform budget allocation. Generally, these condition indices follow a similar level of the rating scale (100 to 0, where 100 denotes perfect condition). However, because of different numbers of distress inputs with different units, different severity levels, and different deduct points and mathematical formulas, condition indices are often incomparable. Consequently, research focused on comparing different distress indices using a highway agency’s pavement distress data is lacking. The objective of this study is to provide a comparative analysis of different condition indices using Michigan Department of Transportation’s (MDOT) flexible distress data, as part of an effort to develop a new index for Michigan. In this process, 2,081 flexible pavement sections were identified from MDOT’s Pavement Management System database, and five different condition indices were evaluated and compared against MDOT’s Distress Index. Comparison results were presented through scatter plots and statistical analysis results. MDOT’s maintenance records were also used to compare the ranges of magnitudes of various indices computed at the time when various types of maintenance activities took place. Finally, this study resulted in recommendations for selecting a new condition index and calibration needs of deduct points and equations.