Visual examination is widely used for evaluating the extent and severity of pavement distress. The visually assessed pavement surface condition is combined with structural information to rate the pavement on the basis of scoring systemsthat have been developed by the state transportation agencies to evaluate whichpavement sections require regular maintenance, overlay, or complete reconstruction. Although scoring is often computerized, the raw data are usually collected by slow, laborious manual and visual methods during site inspections by trained field personnel and they are input into the computer manually. These methods areexpensive and prone to subjectivity, error, and nonrepeatability. Automated surface distress evaluation has not developed rapidly because the accuracy of automated systems has not been sufficient to inspire confidence among the pavement engineers who have to rely on the evaluation results. System accuracy can be enhanced by using better engineering methods and data in designing the image acquisition and image processing portions of automated inspection systems. The visual signal--the apparent luminance of cracks in portland cement concrete (pcc) pavements and the contrast that they exhibit compared to surrounding pavement surfaces--is the input to the automated inspection system. The luminance depends on the reflectivities of the paving materials. Reflectivity measurement methods are specified and data are tabulated. In a companion paper in this record computer modeling methods for detrmining and analyzing crack luminance are reported. The methods and data presented in these papers are useful for designing automated pavement inspection systems. This paper appears in transportation research record no. 1260, Measurement of pavement surface condition 1990.
Abstract