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Multi-physical modeling and automatic network-level prediction of the oxidation aging of in-situ asphalt pavements

Aging increases the stiffness and brittleness of asphalt mixtures, making it crucial to predict changes in mixture properties over time. This study aims to investigate oxidation reactions in the wearing course of asphalt pavement under oxygen diffusion. A multi-physics model, incorporating modules f...

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Bibliographic Details
Published in:Construction & building materials 2024-12, Vol.455, p.139209, Article 139209
Main Authors: Ding, Jitong, Jiang, Jiwang, Lu, Guoyang, Wang, Jingling, Ni, Fujian
Format: Article
Language:English
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Summary:Aging increases the stiffness and brittleness of asphalt mixtures, making it crucial to predict changes in mixture properties over time. This study aims to investigate oxidation reactions in the wearing course of asphalt pavement under oxygen diffusion. A multi-physics model, incorporating modules for thermal conductivity, oxygen diffusion, and oxidation reactions, was developed to analyze the formation of partial oxidation products in asphalt mortar. The model was optimized and validated using field data from ten road sections of Jiangsu highways. The impact of service time, location, and mixture type on aging was examined, allowing for network-level predictions of oxidation aging. Results indicate that as service time increases, the aging gradient between the surface and bottom of the wearing course becomes more pronounced. After 15 years, the surface carbonyl index increases to 330–350, while the bottom index reaches 150–180. This variation is influenced by climatic conditions and changes in mortar film thickness. These findings enhance the understanding of factors influencing field aging and improve predictions under varied conditions. •A multi-physics model was developed for wearing course oxidation analysis.•An empirical model for oxygen diffusion effectively captures material hardening and temperature variations.•The model extends to network-level aging prediction, enhancing maintenance decision-making.
ISSN:0950-0618
DOI:10.1016/j.conbuildmat.2024.139209