Estimation of Pavement Macro-texture from Hot-Mix Asphalt Properties

Macro-texture plays a critical role in the tire-road interaction as it affects skid resistance, tire-pavement noise, rolling resistance and splash and spray. Thus, its measurement and/or estimation based on the properties of the pavement surface layer is important for highway engineers. Several macro-texture prediction models have been proposed to estimate the macro-texture as a function of bituminous mixtures volumetric (such as bitumen content and voids) and gradation properties. However, they appear to not provide satisfactory results and universal applicability because of low sample size and/or poor agreement with experimental data. This paper presents the results of a study to develop a general pavement macro-texture model. The study included an in-depth review of literature about existing prediction models, a thorough critical analysis of the available models, extensive data collection, and data analysis. The experiments included the collection of field and laboratory data for more than 300 bituminous mixtures (including traditional dense-graded, open-graded, and gap-graded mixes). Existing predictions models are re-calibrated using the collected data and a new prediction model is proposed. This new general prediction model appears to provide satisfactory results in comparison with those provided by the existing recalibrated models. However, it still shows relatively poor agreement for some of the mixes. Further analysis and experimental investigations are necessary to determine the effect of other factors beyond mix volumetric, such as traffic compaction, among other factors.