An Overview and Perspectives on the Materials Genome Initiative-Based Asphalt Mix Design Framework

Traditional asphalt mix design approaches are trial-and-error processes and are limited in their ability to find high-performance asphalt mixtures to ensure long-term highway durability. The Materials Genome Initiative (MGI) launched in 2011 is designed to accelerate the development of advanced materials by integrating experiments, computational modeling, and big data analytics and has achieved remarkable progress in other materials design, which demonstrates strong potential for applying the MGI framework to optimize asphalt mix design. This study systematically reviews the application prospects of MGI methodologies in asphalt mixture design. Specifically, it first reviews recent progress in MGI for materials design and summarizes current research efforts to optimize asphalt mix design using traditional laboratory testing, numerical simulations (e.g., finite element method and discrete element method), and artificial intelligence while identifying their respective limitations. To overcome the limitations of each pillar, it is necessary to integrate these individual modules into a comprehensive MGI-based asphalt mix design framework. Furthermore, this study reviews the research on advanced experimental techniques for the multiscale characterization of asphalt binders and aggregates and defines the asphalt mixture genome, including binder-related genes, aggregate-related genes, and compaction-related genes. Finally, the specific steps for developing an MGI-based asphalt mix design framework are elaborated.