Deep Learning-Based Prediction of Asphalt Mixture Mechanical Properties from Aggregate Morphology and Mesostructural Features
Keywords:
asphalt mixture, aggregate morphology, mesostructure, deep learning, pavement engineering, uncertainty quantification, infrastructure governance, sustainabilityAbstract
Asphalt mixtures are complex particulate composites whose mechanical behavior emerges from interactions among aggregate shape, gradation, binder properties, air-void structure, and compaction history. Conventional mixture design and performance prediction often rely on empirical correlations and laboratory testing, which can be costly, time-consuming, and insufficiently sensitive to mesostructural variability. This paper examines the system-level potential of deep learning to predict asphalt mixture mechanical properties from aggregate morphology and mesostructural features. It argues that accurate prediction is not merely a model-fitting problem but a socio-technical infrastructure problem involving imaging pipelines, dataset governance, uncertainty quantification, deployment workflows, and sustainability-oriented decision making. The discussion synthesizes advances in aggregate shape characterization, image-based mesostructure analysis, convolutional and attention-based neural architectures, explainability methods, and reliability engineering. Particular attention is given to structural trade-offs among accuracy, interpretability, computational cost, and field deployability. The paper also considers fairness and robustness when models are transferred across quarries, climates, binder sources, and mixture designs. It concludes that deep learning can augment asphalt mixture design if integrated with material standards, model documentation, calibrated uncertainty, and continuous monitoring. Without such governance, predictive systems risk becoming brittle black boxes that obscure rather than illuminate the physical mechanisms governing pavement performance. The proposed perspective positions deep learning as one component within a broader materials informatics ecosystem that connects aggregate imaging, laboratory validation, construction quality control, and long-term pavement management.
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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.
