Explainable Machine Learning for Assessing the Effects of Aggregate Angularity on Asphalt Mixture Moisture Susceptibility
Keywords:
explainable machine learning, aggregate angularity, moisture susceptibility, asphalt mixtures, infrastructure governance, sustainable pavements, socio-technical systemsAbstract
Moisture-induced damage in asphalt pavements is a complex socio-technical problem because it emerges from coupled physical, material, construction, environmental, and institutional factors. Aggregate angularity is widely recognized as a critical morphology feature that influences interparticle locking, air-void structure, binder film thickness, permeability, and interfacial durability. However, conventional moisture susceptibility assessment methods often rely on empirical testing and simplified volumetric relationships that cannot fully capture the nonlinear and interactive effects of aggregate shape. This paper examines explainable machine learning as a system-level architecture for assessing how aggregate angularity affects asphalt mixture moisture susceptibility. The discussion positions explainability not merely as a model diagnostic but as an infrastructure governance mechanism that supports transparent mixture design, risk communication, regulatory review, and sustainable material selection. The paper synthesizes mechanisms of moisture damage, aggregate morphology measurement, image-based meso-structure characterization, machine learning model design, and interpretation strategies. It also analyzes structural trade-offs among rutting resistance, moisture durability, workability, compaction energy, recycled material use, and life-cycle sustainability. Particular attention is given to deployment challenges in departments of transportation, contractor laboratories, and material supply chains, including data provenance, distribution shift, fairness across regions, model documentation, human oversight, and policy accountability. The paper argues that explainable machine learning can improve moisture susceptibility assessment only when technical performance is embedded within robust data infrastructure, transparent validation, and inclusive decision-making. The conclusion outlines future research directions involving multimodal sensing, physics-informed learning, uncertainty quantification, digital twins, and climate-resilient pavement systems.
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