## 5 Conclusions  
In this study, geometric parameters were computed to analyze macro-texture, and a 2D wavelet transform approach was proposed to decompose texture for the analysis of micro-texture. Geometric parameters, energy, and relative energy were utilized as indicators. Through observation and data analysis of experimental results, the pavement macro- and micro-texture evolution laws were found, leading to the following conclusions:  
(1) Geometric parameters can characterize macro-texture, while energy-based indicators after 2D wavelet transform decomposition can observe micro-texture. Because of the binder masking effect at the initial stages, micro-texture evolution law was not expected to be fully revealed compared with macro-texture in this study. Macro-texture was more critical and easier to control during the construction phase. These results can establish a foundation for the real-time monitoring of pavement texture during field compaction. (2) On a macro-scale, geometric parameters can serve as description indices with a strong relationship with compaction. Among these parameters, \(Sp\) and \(Vmp\) can monitor compaction quality in all types of mixtures, including those with different sizes and structures. The R-square values of polynomial fit with \(Sp\) or \(Vmp\) values and number of roller passes are greater than 0.90 in all mixtures. Other geometric parameters can also provide valuable information about macro-texture characteristics, and the evolution law of macro-texture can regulate the compaction process and optimize mixture properties for its intended use. (3) On a micro-scale, the 2D wavelet transform approach is suitable for characterizing micro-texture properties, and energy can supervise the compaction process. The end energy of Level 1 is about 45% of the initial energy, and the end energy of Level 2 is about 50% of the initial energy. As the compaction is gradually completed, the energy will also tend to be stable, such as Level 1 energy of AC-13 convergence to 20 and Level 2 energy of SMA-13 convergence to 650. By using energy as a supervising factor, we can control the compaction process and ensure efficient material compaction. In our follow-up study, these indicators can be combined with a mechanical model to evaluate the compaction process adequately. Further research can focus on the evolution \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**RE of Mixtures after** & & & & & & \\
**Roller Passes 12 Times** & & & & & & \\ \hline
**Decomposition Level** & **Level 1** & 0.2 & 0.2 & 0.3 & 0.1 & 0.3 \\  & **Level 2** & 4.1 & 5.1 & 7.6 & 3.1 & 6.5 \\  & **Level 3** & 6.6 & 7.2 & 9.3 & 14.2 & 7.5 \\  & **Level 4** & 24.4 & 14.3 & 9.5 & 24.9 & 10.2 \\  & **Level 5** & 12.5 & 26.5 & 12.4 & 16.5 & 16.3 \\  & **Level 6** & 52.1 & 46.7 & 60.9 & 41.2 & 59.1 \\
**RE of Mixtures after** & & & & & & \\
**Roller Passes 24 times** & & **AC-13** & **AC-16** & **AC-25** & **SMA-13** & **OGFC-13** \\
**Decomposition Level** & **Level 1** & 0.1 & 0.2 & 0.3 & 0.1 & 0.4 \\  & **Level 2** & 3.0 & 5.5 & 8.2 & 3.9 & 8.5 \\  & **Level 3** & 5.2 & 12.4 & 9.0 & 16.2 & 8.5 \\  & **Level 4** & 26.5 & 13.8 & 9.7 & t.3 & 13.0 \\  & **Level 5** & 14.2 & 23.9 & 12.4 & 17.3 & 13.2 \\  & **Level 6** & 50.9 & 44.2 & 60.3 & 42.2 & 56.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RE distribution results. law of texture under different gradations during field compaction. Based on the findings of this study, it is suggested that by setting suitable thresholds for certain indices, texture can be controlled during the compaction process. These indices can also be linked to other advanced techniques used for monitoring the compaction process, which can help improve the quality of compaction control. Conceptualization, C.D. and D.W.; Formal analysis, Y.L. and H.X. ; Investigation, Y.L., H.X. and C.D. ; Methodology, Y.L., D.W., S.J. and Z.W. ; Software, Y.L. and Z.W. ; Validation, H.X. ; Writing--original draft, Y.L. ; Writing--review and editing, D.W., C.D. and S.J.