Three-Dimensional Reconstruction and Finite Element Simulation of Asphalt Mixture Mesostructures Using Image Recognition

Abstract

Asphalt mixtures are heterogeneous socio-technical materials whose performance emerges from the spatial arrangement of aggregates, mastic, air voids, and interfaces across multiple scales. Empirical mixture design and continuum-level simulation have historically provided practical guidance for pavement engineering, but they often obscure the mesostructural mechanisms that govern stiffness, damage initiation, moisture susceptibility, and fatigue life. This paper examines the integrated workflow of image recognition, three-dimensional reconstruction, and finite element simulation for asphalt mixture mesostructures from a systems-level perspective. It argues that the technical pipeline cannot be understood only as a sequence of imaging, segmentation, mesh generation, and mechanical computation. Instead, it is an infrastructure problem involving data acquisition standards, model governance, computational deployment, uncertainty management, and long-term stewardship. The discussion reviews image recognition methods for aggregate morphology and void characterization, compares reconstruction strategies including tomographic, statistical, and learning-based approaches, and analyzes finite element modeling choices concerning mesh fidelity, constitutive representation, boundary conditions, and computational cost. It then expands to deployment architecture, edge and high-performance computing trade-offs, robustness under sensor drift and distribution shift, fairness in infrastructure investment, and sustainability implications. Cross-domain comparisons with medical imaging, geoscience, and materials informatics clarify reusable design principles. The paper concludes that reliable mesostructural digital twins require not only improved algorithms but also transparent validation, interoperable data ecosystems, and policy frameworks that align model performance with public infrastructure outcomes.