Proper tire–pavement interaction is essential for the safety of motorists. Pavement surface texture is a major contributing factor to tire–pavement friction. This study performed a series of statistical analyses of field-measured friction and texture data to find the texture–friction correlation. Three test sections with different pavement types were selected within the state of Texas. Data were collected at three locations in the right wheel path and three locations in the center of the lane for each test section. To measure the texture data, the researchers used the circular track meter (CTM) and a prototype measurement device developed in-house and consisting of a line laser scanner (LLS). Friction measurements were obtained with the dynamic friction tester (DFT) and Grip-Tester. The mean profile depth (MPD) was calculated by using the measured texture data. The relationship between the MPD values and the friction numbers obtained from the Grip-Tester and DFT was investigated at speeds of 50 and 70 km/h (31.1 and 43.5 mph). The repeatability and reliability of both the developed LLS prototype and the Grip-Tester were also evaluated, as well as the effect of test speed on friction measurement. The results indicated a strong positive correlation between the texture and friction data. In addition, the developed LLS prototype was able to scan the pavement surface texture more reliably and precisely than the CTM in terms of vertical and horizontal resolution. The Grip-Tester showed promising results compared with the DFT with regards to the friction measurement.

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The morphology of aggregate particles used for pavement construction plays an essential role in the structural capacity and safety performance of pavement structures. Each of the three main components of aggregate morphology (form, angularity, and texture) has a distinct effect on pavement performance and corresponds to a different frequency range. Considering the challenge in segregating form, angularity, and texture in the space domain, characterizing them separately in the frequency domain would be beneficial and would allow for a more objective and detailed classification system for aggregate morphology. This study focuses on the characterization of aggregate angularity in the frequency domain with the objective of obtaining a parameter that is free of individual subjectivity. Since aggregate angularity is a subjective visual descriptor of aggregate shape variations at corners, a survey was conducted of pavement engineers to collect visual ratings of aggregate angularities using a set of aggregates. Thereafter, using the average visual ratings from the survey responses as reference, three common aggregate angularity indexes were evaluated: roundness, the University of Illinois Aggregate Image Analyzer (UIAIA) angularity index, and the Aggregate Image Measurement System (AIMS) angularity index. In addition, with the aid of the discrete Fourier transform (DFT) algorithm, the contributing frequencies were acquired for visual rating, along with roundness and the UIAIA and AIMS angularity indexes. Based on the contributing frequencies identified, prediction models were successfully established for visual rating: roundness and the UIAIA and AIMS angularity indexes. It was concluded that DFT can be accurate in objectively assessing angularity and that roundness is the more robust parameter and can be accurately predicted by the models developed.