## 4 Results and Discussion
### Assessment of Density Homogeneity  
In terms of the four density prediction models, the predicted bulk densities of asphalt concrete base in the field test are shown in Table 6. In order to not affect the construction period, Line 4 was selected and used to predict the density. The predicted density compared with the measurement density by non-nuclear density gauge is shown in Figure 9. The correlation coefficient of prediction density with measurement density was calculated and is shown in Table 7. The predicted density of the four models had a similar correlation coefficient with the measured density. The density by the Birefringence model had an obvious volatility and the difference value between maximum density and minimum density was 0.71. The results of the Linearity Indicator model deviated highly with the measured density than the other models. The Boettcher model and the Rayleigh model had similar results, but the density predicted by the Rayleigh model closed to the measured density.  
The Rayleigh model presented the highest correlation coefficient when compared with the results from the four prediction models. This model had the potential way to calculate and corrected the density by GPR data. The Rayleigh model was then used to predict the density and the correction factor. The correction factor was the ratio of density obtained by the non-nuclear density gauge and the Rayleigh model (G Non-nuclear/G Rayleigh). Therefore, the continuous prediction of density and the correction factor compared to the non-nuclear density gauge results in the test field are shown in Table 8. The predicted density results had an average correction factor of 1.15, compared to the measured density from the non-nuclear density gauge. With this correction factor, the GPR data and Rayleigh model was able to calculate the accurate density of asphalt pavement without coring or non-nuclear density gauge. In the application of practical road projects, this method could save a lot of testing time and improve the efficiency of quality control.  
The Rayleigh model was then used to predict the density of the parallel test lines in the tested area. The density distributions of the asphalt concrete base in the seven tested lines are presented in Figure 10. Line 1, Line 6, and Line 7 had relatively low density in the asphalt concrete base. These results indicate that the edge of asphalt concrete base showed segregation of the asphalt mixture. Insufficient compaction of the asphalt mixture was one of the reasons for low-density distribution. According to the results of density detection by the GPR and Rayleigh model, the construction technology of p  
Figure 9: Comparison of the predicted density by the four prediction models and the measured density by the non-nuclear density gauge.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Location**} & **Rayleigh** & **Non-Nuclear** & **Correction Factor** \\  & **Model** & **Density Gauge** & **(G Non-Nuclear/G Rayleigh)** \\ \hline
5 m & 1.91 & 2.46 & 1.29 \\
10 m & 2.04 & 2.41 & 1.18 \\
15 m & 2.10 & 2.42 & 1.15 \\
20 m & 2.10 & 2.39 & 1.14 \\
25 m & 2.22 & 2.37 & 1.06 \\
30 m & 2.17 & 2.36 & 1.09 \\
35 m & 2.14 & 2.34 & 1.09 \\
40 m & 2.02 & 2.37 & 1.17 \\
45 m & 2.12 & 2.39 & 1.13 \\
50 m & 2.03 & 2.39 & 1.17 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Density correction factor of the Rayleigh model.