Image-based indices of aggregates for predicting the initial skid resistance of bituminous pavements

Abstract

The objective of this study is to propose image-based indicators of microtexture and macrotexture for pavement surfaces that can predict the initial skid resistance. To achieve this objective, images of different types of aggregates that were collected from quarries were processed. Based on the outcome of the image processing, suitable indicators of shape, angularity, and surface texture of coarse aggregates were proposed. Skid resistance data were collected from pavements constructed with the aggregates used in the image processing, as well as with different surface course mixes commonly used in India, such as Bituminous Concrete (BC), Stone Matrix Asphalt (SMA), and Gap-Graded Rubberized Bituminous mix (GGRB).Through a detailed investigation of the image processing outcome and field data, this study introduces the Aggregate Surface Texture Index (STI) as a means of quantifying microtexture, which can be obtained by processing images of the aggregate using wavelet decomposition technique. Additionally, the study proposes Shape Index (SI) and Angularity Index (AI) as indicators for macrotexture, which can be obtained from the processing of digital images of aggregates. For determining the skid resistance of newly constructed pavements, the study suggests using the Surface Macrotexture Index (SMI), which can be obtained by processing digital images of the pavement surface. Furthermore, this study proposes statistical models for predicting the initial skid resistance of pavement based on these indicators. Overall, the proposed image-based indicators of microtexture and macrotexture have the potential to provide valuable insights for designing and maintaining safe and durable pavement surfaces.