## 6 Results and Discussion
### Correction of Asphalt Pavement Rutting Prediction Model
\[R_{a}=\sum_{i=1}^{n}2.31\times 10^{-8}.\beta_{1}k_{Ri}T_{pef}^{2.93.\beta_{2}}p_{i}^{1.80}N_{e}^{0.48.\beta_{3}}R_{0i} \tag{7}\]  
Equation (7) is adopted to fit the measured results shown in Figure 6. The local correction coefficients of different structures and all kinds of errors in the model prediction can be obtained (Table 4). Table 4 shows that the accuracy of rutting prediction models of all structures is greatly improved after local correction. The determination coefficients \(R^{2}\) are higher than 0.87, which indicates that the new rutting prediction model after local correction is suitable for the rutting prediction of all kinds of pavement structures where the RIOHITrack is located. Figure 7 shows the comparison of results before and after the local correction of the rutting models of several representative pavement structures. After local correction, the predicted values of the rutting models with these structures are relatively close to the measured values. The measured result fluctuates up and down near the prediction curve, the error of the model prediction decreases, and the accuracy is improved significantly. For Structures II and VII, the prediction curve is no longer above the measured curve but fluctuates up and down near the measured curve. \(R^{2}\) becomes higher than 0.87 from a negative value. For STR5 with the smallest error, the MSE is 0.13 mm\({}^{2}\); the RMSE is 0.36 mm; the MAE is 0.26 mm; and the MAPE is 6.4%. The rutting model can fit the measured value of rutting better, and the prediction effect of the model is better. For Structures III, IV, V, and VI, the prediction curve is no longer parallel to the measured curve and no longer crosses in the middle but fluctuates up and down near the measured curve. \(R^{2}\) increases to 0.87 and above from 0-0.50. For STR8 with the smallest error, the MSE is 0.30 mm\({}^{2}\); the RMSE is 0.55 mm; the MAE is 0.39 mm; and the MAPE is 7.5%. The accuracy of the model has been improved significantly. Comparing the results in Figures 6 and 7, it can be seen that the prediction effect of the new rutting model after correction is significantly better than that of the model before, which is mainly related to the introduction of local correction coefficients \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\). The evolution curves of the measured rutting values of 14 kinds of pavement structures in Figure 6 with the cumulative ESALs basically show a similar phenomenon; that is, the rutting deformation in the initial loading stage (\(N_{e}\) is about 0 to 5 million ESALs) increases rapidly, and the rutting evolution curve is relatively steep. When the initial stage is over, the rutting deformation begins to increase slowly, and the rutting evolution curve also becomes relatively flat. The reason for this phenomenon may be that in the initial loading stage, the voids inside the mixture of the newly paved asphalt pavement are compressed under the load, resulting in obvious compaction deformation of the asphalt concrete structure layer, as can be seen from Figure 6. At the end of the initial loading stage (generally \(N_{e}\) is about 5 million ESALs), the compaction deformation of the asphalt pavement can reach