Abstract
This paper discusses the results of the field experimentation of a new system developed to help inspect the construction of asphalt pavements using digital video images. The system computes the gray-level co-occurrence matrix of images of newly constructed pavements to find various parameters that are commonly used in visual texture analysis. Using principal component analysis to integrate multivariable data into a single classifier, Hotelling’s T2 statistic, the system creates a list of the location of possible nonuniformities that require closer inspection. A total of 18 continuous road segments of recently paved roads were tested and analyzed with the system. These road segments allowed the refinement of the methodology and the software needed to produce tables and plots that will be used by inspection personnel in the field. The results demonstrated the capability of the system for detecting potential nonuniformities of recently completed pavements that can be used during construction as well as in the final inspection process. More testing is recommended under actual construction conditions to verify the system’s capabilities.
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