Abstract

The Kentucky Transportation Cabinet’s (KYTC) foremost goal is building and maintaining a safe road network. To accomplish this goal, KYTC needs reliable pavement friction and macrotexture data, both of which influence road performance and safety. KYTC currently measures fiction using a lock-wheel skid tester (AASHTO T 242), however, the agency has not been able to collect data at the network level due to equipment and personnel limitations. In 2020 the Cabinet commissioned a study to collect friction and macrotexture data. Investigators used a sideway-force coefficient routine investigation machine (SCRIM) (AASHTO TP 143) to measure friction. To understand the performance of a range of devices used to measure friction and macrotexture (a lock-wheel skid tester, Dynamic Friction Tester (ASTM E1011), LMI Selcom Optocator, LCMS, and AMES Laser Texture Scanner), this study evaluated the degree to which data obtained by different types of equipment are correlated. Five routes and 13 different segments were selected for the study to capture a range of friction and macrotexture values. The following surfaces were tested: concrete pavement, Superpave 0.38 bituminous pavement, crack sealed bituminous surfaces, single and double layer of microsurfacing, cape seal composed of a chip seal with microsurfacing placed on the chip seal, and a Superpave 4.75 mm bituminous surface. Friction data had seasonal corrections applied. Friction and macrotexture values obtained using different equipment types were highly correlated with one another. Due to the condition of concrete pavement and its effects on correlations and regression equations, future studies should evaluate concrete pavements and bituminous pavements separately. Only one piece of each type of equipment was used for the study. Consideration should be given to developing correlations and regression equations for each piece of equipment/device used to make measurements. Future studies should also evaluate how the seasonal factors influence correlations and regression equations.

Report Date

8-2023

Report Number

KTC-24-04

Digital Object Identifier

https://doi.org/10.13023/ktc.rr.2024.04

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