Digital Twin Modeling of Asphalt Mixture Microstructures for Performance-Oriented Pavement Design
Keywords:
digital twin; asphalt mixture; microstructure; performance-oriented pavement design; image recognition; infrastructure governance; life-cycle assessment; uncertaintyAbstract
Asphalt pavements are socio-technical infrastructures whose performance emerges from interactions among aggregate morphology, binder rheology, air-void distribution, compaction energy, climate, and traffic loading. Conventional mixture design often abstracts away mesoscale heterogeneity, treating asphalt as a homogeneous continuum or relying on empirical relationships. This paper develops a system-level research agenda for digital twin modeling of asphalt mixture microstructures to support performance-oriented pavement design. The digital twin is framed not merely as a simulation asset but as an institutional and computational infrastructure that couples imaging, sensing, data governance, physics-based simulation, machine learning, and life-cycle decision-making. The paper examines architectural trade-offs involving fidelity, latency, cost, scalability, uncertainty, and robustness. It discusses how image recognition and tomography can reconstruct aggregate skeletons, mastic films, and void networks, and how these representations can be updated through field data. Deployment challenges in departments of transportation, contractor organizations, and material suppliers are analyzed, including data ownership, model validation, interoperability, and fairness in resource allocation. Sustainability implications are explored through life-cycle assessment and circular material use, while policy implications focus on standards, procurement, and accountability. The paper concludes that digital twin modeling can improve pavement performance prediction only if technical accuracy is matched by governance capacity, transparent uncertainty communication, and equitable institutional design. It proposes a research and policy roadmap for integrating microstructure-aware digital twins into performance-oriented pavement design without reducing them to isolated computational tools.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
