Factors Affecting Dielectric Properties of Asphalt Mixtures in Asphalt Pavement Using Air-Coupled Ground Penetrating Radar
Source abstract
Abstract
Ground-penetrating radar (GPR) is widely used for thickness or compaction degree detection of asphalt pavement layers, where the dielectric properties of asphalt mixtures serve as a key parameter influencing detection accuracy. These properties are closely related to the composition of the mixture and are susceptible to environmental factors such as water or ice. To clarify the influence of various factors on the dielectric behavior of asphalt mixtures, an experimental study was conducted under controlled environmental conditions. Asphalt mixture specimens with different air void contents (5.49~10.29%) were prepared, and variables such as void fraction, moisture, and ice presence were systematically controlled. Air-coupled GPR was employed to measure the specimens, and the relative permittivity was calculated using both the reflection coefficient method (RCM) and the thickness inversion algorithm (TIA). Discrepancies between the two methods were compared and analyzed. Results indicate that the RCM is significantly influenced by surface water or ice and is only suitable for dielectric characterization under dry pavement conditions. In contrast, the TIA yields more reliable results across varying surface environments. A unified model (the optimized shape factor u = −4.5 and interaction coefficient v = 5.1) was established to describe the relationship between the dielectric properties of asphalt mixtures and their volumetric parameters (bulk specific density, air void content, voids in mineral aggregate, and voids filled with asphalt). This study enables quantitative analysis of the effects of water, ice, and mixture composition on the dielectric properties of asphalt mixtures, providing a scientific basis for non-destructive and accurate GPR-based evaluation of asphalt pavements.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Geophysical Methods and Applications · Microwave Imaging and Scattering Analysis · Microwave and Dielectric Measurement Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil Engineering and Transportation, Foshan University, Foshan 528225, China
Funding
Guangdong University Student Science and Technology Innovation Cultivation Special Fund · Guangdong Basic and Applied Basic Research Foundation · National Natural Science Foundation of China · National Key Research and Development Program of China
References (39)
Show source-provided references
1. Huang, L., Jin, Z., Yao, Z., Chen, B., Li, W., Xiong, X., and Yu, H. (2025). Thickness Uniformity Assessment of Epoxy Asphalt Pavement Layer on Steel Bridge Decks Using Three-Dimensional Ground-Penetrating Radar. Buildings, 15.10.3390/buildings15122138 ↗
2. Automatic detection and location of pavement internal distresses from ground penetrating radar images based on deep learning10.1016/j.conbuildmat.2023.134483 ↗
3. Measuring layer thicknesses with GPR–Theory to practice10.1016/j.conbuildmat.2005.06.005 ↗
4. Asphalt pavement density measurement using non-destructive testing methods: Current practices, challenges, and future vision10.1016/j.conbuildmat.2022.128154 ↗
5. Saarenketo, T. (2006). Electrical Properties of Road Materials and Subgrade Soils and the Use of Ground Penetrating Radar in Traffic Infrastructure Surveys. [Ph.D. thesis, University of Oulu].
6. Spatial distribution characteristics and mechanical properties of artificial coarse aggregates with different morphologies in asphalt mixtures
7. Deep learning-based detection of tie bars in concrete pavement using ground penetrating radar10.1080/10298436.2022.2155648 ↗
8. Jam traffic pattern of a multi-phase lattice hydrodynamic model integrating a continuous self-stabilizing control protocol to boycott the malicious cyber-attacks10.1016/j.chaos.2025.116531 ↗
9. Permittivity Heterogeneity Influence of Multiphase Pavement Materials on Ground-Penetrating Radar Detection Results: A Study Based on Probability Distribution10.1061/JMCEE7.MTENG-18185 ↗
10. Automatic NDT of semi-rigid base cracks within asphalt pavement based on 3D-GPR and deep learning10.1080/10298436.2025.2538791 ↗
11. Real-time asphalt pavement layer thickness prediction using Ground-Penetrating Radar based on a modified Extended Common Mid-Point (XCMP) approach10.1109/TITS.2023.3343196 ↗
12. A novel moisture damage detection method for asphalt pavement from GPR signal with CWT and CNN10.1016/j.ndteint.2024.103116 ↗
13. YOLOv11-CAFM model in ground penetrating radar image for pavement distress detection and optimization study10.1016/j.conbuildmat.2025.141907 ↗
14. Effective medium theories in electromagnetism for the prediction of water content in cement pastes10.1016/j.ijengsci.2020.103273 ↗
15. Prediction model of asphalt content of asphalt mixture based on dielectric properties10.1155/2020/6661593 ↗
16. Dielectric model of asphalt pavement materials towards the future electrified road10.1098/rsta.2022.0164 ↗
17. Removal of the GPR antenna vibration effect through height shifting correction for enhanced multi-layer asphalt pavement thickness determination10.1016/j.autcon.2024.105335 ↗
18. Theoretical modeling on the temperature-dependent dielectric properties of concrete pavement materials10.1016/j.conbuildmat.2023.133674 ↗
19. In-situ grouting rate prediction of semi-flexible pavement based on a novel composite dielectric constant model and ground-penetrating radar10.1016/j.conbuildmat.2024.137209 ↗
20. Improved density prediction model based on global optimization algorithm for GPR system10.1016/j.measurement.2024.115243 ↗
21. Development of Aggregate Dielectric Constant Database Protocol for Asphalt Concrete Density Prediction10.1177/03611981241230316 ↗
22. A novel model for asphalt mixture density prediction based on physics-informed neural network with Bayesian optimization10.1016/j.conbuildmat.2025.143170 ↗
23. Assessment of HMA air-voids and stiffness based on material dielectric values10.1080/14680629.2011.9690360 ↗
24. Application of ground-penetrating radar in measuring the density of asphalt pavements and its relationship to mechanical properties10.1080/10298436.2015.1007225 ↗
25. Dielectric modeling of asphalt mixtures and relationship with density10.1061/(ASCE)TE.1943-5436.0000204 ↗
26. Estimation of in-situ density and moisture content in HMA pavements based on GPR trace reflection amplitude using different frequencies10.1016/j.jappgeo.2013.04.007 ↗
27. Parametric optimization of Ground Penetrating Radar approach for assessing asphalt pavement surface layers compaction10.1016/j.jappgeo.2020.104187 ↗
28. Bayesian optimization based random forest and extreme gradient boosting for the pavement density prediction in GPR detection10.1016/j.conbuildmat.2023.131564 ↗
29. In-place hot-mix asphalt density estimation using ground-penetrating radar10.3141/2152-03 ↗
30. Research on detection method of concrete compressive strength based on dielectric properties10.1016/j.jobe.2023.107090 ↗
31. Harmsen, E., Parsiani, H., and Torres, M. (2003, January 27–30). Evaluation of several dielectric mixing models for estimating soil moisture content in sand, loam and clay soils. Proceedings of the 2003 American Society of Agricultural and Biological Engineers (ASABE) Annual Meeting, Las Vegas, NV, USA.
32. Experimental study on the variation of soil dielectric permittivity under the influence of soil compaction and water content10.1155/2022/3575541 ↗
33. Development and validation for in situ asphalt mixture density prediction models10.1016/j.ndteint.2011.03.002 ↗
34. Asphalt content prediction model of asphalt mixtures based on dielectric properties10.1617/s11527-022-02095-4 ↗
35. Compactness prediction of asphalt concrete using Ground-Penetrating Radar: A comparative study10.1016/j.conbuildmat.2022.129588 ↗
36. Development of asphalt mixture density estimation model applicable to wide air void content range using ground penetrating radar10.1016/j.conbuildmat.2021.123521 ↗
37. Effective permittivity of dielectric mixtures10.1109/36.3045 ↗
38. Estimation of density and moisture content in asphalt mixture based on dielectric property10.1016/j.conbuildmat.2021.123518 ↗
39. Finding Solutions of Equations Using the Cardano Formula to Solve Third Level Equations