A digital design method for asphalt mixtures that incorporates aggregate geometry

This paper presents a new Data-Driven digital method for selecting aggregates and bitumen content in hot mix asphalts (HMA) to achieve desired properties such as air voids content, workability, Marshall stability, and Marshall flow through a streamlined process. The proposed method consists of three steps: first, Dynamic Image Analysis is used to extract the geometric characteristics of the aggregates in asphalt mixtures. Second, physics engine software is used to assist estimating the number of aggregates and volume of mastic (bitumen, filler, and aggregates with Min Feret < 2 mm) in a fixed 250 g of asphalt mixtures. The generated number of aggregates and volume of mastic correspond to the aggregate gradation and bitumen content in the mixture. Third, the number of aggregates and volume of mastic that meet the desired properties are obtained based on correlations between the air voids content, workability, Marshall stability, Marshall flow, and the number of aggregates and volume of mastic. The proposed method achieved a 94% predictive accuracy for nine mixtures with consistent aggregate and bitumen. However, in a sensitivity analysis involving diverse asphalt mixtures, the predictive accuracy decreased to 78%. The authors have demonstrated that the number of aggregates, informed by their shapes, and volume of mastic significantly influence HMA performance and should be carefully controlled in future asphalt mix design, especially in creating realistic digital HMA representations. The proposed digital approach is expected to minimise the need for time-consuming and laborious physical and mechanical tests to determine the properties of new asphalt mixtures.