AI-Driven Optimization of Recycled Aggregate Gradation for Sustainable Asphalt Mixture Design
Keywords:
recycled aggregate; asphalt mixture design; artificial intelligence; gradation optimization; sustainable infrastructure; pavement performance; data governance; socio-technical systemsAbstract
This paper examines artificial intelligence driven optimization of recycled aggregate gradation as a system-level strategy for sustainable asphalt mixture design. Recycled aggregates, including reclaimed asphalt pavement and recycled concrete aggregates, offer substantial environmental and economic benefits, yet their morphological variability, absorption characteristics, and heterogeneous surface textures complicate conventional volumetric design. Artificial intelligence can support gradation optimization by integrating aggregate imaging, mixture performance data, construction records, and lifecycle assessment into adaptive decision architectures. The analysis considers not only predictive accuracy but also structural trade-offs among rutting resistance, fatigue durability, moisture susceptibility, workability, and long-term pavement resilience. It argues that the technical optimization problem is inseparable from governance, data infrastructure, procurement policy, and fairness across suppliers and agencies. A central claim is that AI-driven gradation design should be understood as a socio-technical infrastructure rather than a standalone computational tool. The paper reviews relevant advances in machine learning, computer vision, pavement performance modeling, and infrastructure governance. It then discusses architecture choices, deployment challenges, robustness requirements, and policy implications for public agencies. Cross-domain comparisons with supply chain optimization, commons governance, and case-study evaluation are used to clarify how optimization models can remain accountable under uncertainty. The conclusion emphasizes that sustainable asphalt mixture design requires hybrid intelligence in which data-driven optimization is bounded by material science, local conditions, institutional capacity, and transparent validation. Such an approach can improve recycled aggregate utilization while protecting pavement performance and public trust.
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