Abstract
Many research studies and field experiences have shown a strong relationship between wet-weather accidents and pavement skid resistance. Therefore, measurement methods and models are needed to evaluate the safety level of driving on an asphalt pavement surface during its service life. The objective of this study was to develop a skid resistance prediction model based on measurable quantities such as aggregate shape characteristics, aggregate gradation, aggregate resistance to polishing, and traffic level. To achieve this objective, the skid number (SN) of asphalt pavement sections and traffic data were acquired and analyzed. In addition, statistical analysis was conducted to determine the relationship between different aggregate properties, pavement surface characteristics, and the measured SN values. The aggregate properties were measured using conventional test methods (acid insolubility, magnesium soundness, micro-Deval, and British polish value), and the Aggregate Imaging System (AIMS). The pavement surface characteristics were measured using the dynamic friction tester and circular texture meter. The statistical analysis led to the development of a new model for predicting the asphalt pavement SN as a function of traffic level, initial and terminal aggregate texture values measured using AIMS, and aggregate gradation described using the two-parameter Weibull distribution function.
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