Dynamic Coplanar Array Capacitance Imaging Method for Asphalt Materials With Concealed Damages
The dynamic imaging method utilizing a coplanar capacitance sensor with a pair of electrodes has high imaging accuracy in detecting concealed damage. While the static imaging method with a coplanar array capacitance sensor has great detection efficiency. Therefore, a dynamic coplanar capacitance imaging method using an innovative mobile coplanar array capacitance sensor is proposed. This method combines the strengths of both methods to image and identify concealed damage in asphalt materials. Initially, the dynamic sensitivity field distribution is established. Subsequently, the normalized dynamic coplanar capacitance of the measured electrode pairs is analyzed. Finally, concealed damages in asphalt materials are identified. Layer D1 is determined to be the optimal dynamic sensitivity layer. Circle damages exhibit the greatest normalized coplanar capacitance, followed by square and triangle damages. Normalized coplanar capacitance decreases with increasing relative depth of concealed damage. The deeper damage with larger relative permittivity differences displays redder in the reconstructed images. Square damages have the highest imaging accuracy, followed by circle and triangle damages. The imaging accuracy of concealed damages in asphalt materials ranks from lowest to highest: void damages in asphalt mixtures, void damages in asphalt mastics, moisture damages in asphalt mastics, and moisture damages in asphalt mixtures. The dynamic coplanar array capacitance imaging method can identify the relative depth and shape characteristics of various concealed damages in different asphalt materials. The outcomes of this research have the potential to assist engineers in visually and precisely recognizing concealed damages in in situ shallow asphalt layers.
Authors
- Shiao Yan (ORCID: https://orcid.org/0000-0002-6266-1547)
- Yao Kang (ORCID: https://orcid.org/0000-0002-4540-7811)
- Qiao Dong (ORCID: https://orcid.org/0000-0001-7461-9226)
- Bin Shi (ORCID: https://orcid.org/0000-0002-5703-7176)
- Xueqin Chen (ORCID: https://orcid.org/0000-0003-1538-3713)
- Xing Jun Hu (ORCID: https://orcid.org/0009-0007-4314-0520)
Institutions
- Nanjing University of Science and Technology (CN)
- Southeast University (CN)
Publication Details
- Journal
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2025-01-22
- DOI
- https://doi.org/10.1109/tits.2025.3528954
- Citations
- 3
- Primary Topic
- Ultrasonics and Acoustic Wave Propagation
- Type
- article
- Field-Weighted Citation Impact
- 2.59
Funders
- National Natural Science Foundation of China