Application of Image Analysis Techniques to Develop a Quality Control Tool for Automated Optimum Binder Content Determination of Open-Graded Friction Course Mixtures

Abstract

In some transportation agencies, including the Florida Department of Transportation (DOT), open-graded friction course (OGFC) mixtures are designed by estimating the optimum binder content (OBC). The estimation is based on visual inspection of the asphalt binder draindown (ABD) configuration of three OGFC samples placed on pie plates with predetermined trial asphalt binder contents (ACs). The inspection of the ABD configuration is performed by trained and experienced technicians who determine the OBC with perceptive interpolation or extrapolation based on the known AC values of the above samples. To eliminate the human subjectivity involved in this method, the authors first developed an automated image processing–based method for prediction of the OBC by using digital images of the pie plate specimens (PPSs). In the extended research effort reported in this paper, a quality control tool (QCT) was developed for the aforementioned automated method to enhance its reliability when implemented by other agencies and contractors. The QCT was developed with three quality control imaging parameters (QCIPs): orientation, spatial distribution, and segregation of ABD configuration of PPS images. Then, the above QCIPs were evaluated from PPS images of a variety of mixture designs produced with the Florida DOT visual method. The statistical and computer-generated results indicated that the selected QCIPs are adequate for the formulation of quality control criteria for PPS production. The authors believe that the developed QCT will enhance the reliability and accuracy of the automated OBC estimation image processing–based method.