Abstract
The aging of asphalt mixtures has a significant impact on the service life of asphalt pavements. Currently, the commonly employed method for assessing aging involves the extraction of asphalt from asphalt mixtures using the Abson method. However, this method is known to be detrimental to the extracted asphalt samples, time-consuming, and environmentally unfriendly. This study explored a novel non-destructive method for assessing asphalt aging, known as low-field nuclear magnetic resonance (LF-NMR). It primarily investigated the influence of asphalt content in asphalt mixtures on the patterns of LF-NMR spectra. Specifically, it examined the effect of asphalt content on LF-NMR spectra in asphalt mixtures with varying particle sizes and aging levels at the same detection temperature. Additionally, machine learning was used to establish predictive models linking NMR spectral features to asphalt mixture aging levels, enhancing interpretation accuracy. The research results revealed the following: (1) Spectral parameters such as peak height, normalized peak area, and normalized total peak area had a significant impact on the first principal component of LF-NMR spectra. (2) Asphalt content in the mixture increased as particle size decreased, leading to corresponding changes in the LF-NMR spectra. (3) There was a strong correlation between the aging degree of asphalt and asphalt mixtures and the normalized total peak area of their LF-NMR spectra. The study provides a non-destructive method to assess asphalt mixture aging, enabling timely maintenance decisions and improving pavement durability.
| Original language | English |
|---|---|
| Article number | 2004 |
| Journal | Materials |
| Volume | 18 |
| Issue number | 9 |
| DOIs | |
| State | Published - Apr 28 2025 |
Scopus Subject Areas
- General Materials Science
- Condensed Matter Physics
Keywords
- aging
- asphalt content
- asphalt mixtures
- low-field nuclear magnetic resonance
- principal component analysis