## 2 Research Conditions and Methods
### Analysis of Pavement Performance Development Law
#### 2.2.3 Determination of Prediction Model  
In this section, we aim to establish a reasonable prediction model based on the influence of different treatment measures to evaluate the decay law of pavement conditions with time. In this paper, a large number of detection data of the above-mentioned selected typical sections are analyzed, and combined with the degradation law of rural highway pavement performance, the pavement performance prediction models of rural road RPCI and RRQI based on different treatment measures are established.  
(1) Prediction model for RPCI  
Based on the regular inspection data of rural roads in Jiangsu province from 2016 to 2021, the RPCI data of typical sections of rural asphalt pavement and cement pavement were fitted and analyzed. The main steps include: (1) grouping the inspection data and determining the corresponding initial decay year; (2) determining the basic parameters through data fitting analysis; (3) considering the impact of the internal health condition index RPCI on the decay pattern of rural road pavement (RIPCI > 90, the decay coefficient is 1, 80 < RIPCI < 90, the decay coefficient is 0.8, 70 < RIPCI < 80, the decay coefficient is 0.6, RIPCI < 70 decay coefficient is 0); (4) according to the verification of other groups of data, optimize and determine the best parameters when the fitting accuracy requirements are met. Finally, RPCI decay models of five different treatment measures, such as slurry sealing layer, thin layer cover, crushed stone regeneration, milling and repaving one layer, and milling and repaving two layers, are established, respectively, as shown in Figures 4 and 5 below.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pavement** & \multirow{2}{*}{**Maintenance History**} & \multirow{2}{*}{**Road Section Location**} & **Thickness** & **Traffic** & \multirow{2}{*}{**RIPCI**} \\
**Structure** & & & **(Region and Year)** & **(cm)** & **Level** & \\ \hline \multirow{6}{*}{Asphal} & Original pavement & K003 + 200-K005 + 300 (Haimen City, 2008) & & & \\  & Slurry sealing layer & K009 + 100-K010 + 000 (Haimen City, 2020) & & & \\  & Thin layer cover & K006 + 700-K007 + 200 (Haimen City, 2015) & & & \\ \multirow{6}{*}{Pavement} & Crushed stone regeneration & K015 + 700-K017 + 200 (Haimen City, 2015) & & & \\  & Milling and repaving one layer & K034 + 900-K036 + 600 (Haimen City, 2015) & & & \\  & Milling and repaving two layers & K049 + 100-K053 + 200 (Haimen City, 2015) & & & \\  & Original pavement & K012 + 000-K016 + 2000 (Guanan County, 2008) & & & \\  & Slurry sealing layer & K007 + 500-K009 + 000 (Guanan County, 2019) & & & \\ \multirow{6}{*}{Cement} & Thin layer cover & K010 + 500-K013 + 000 (Guanan County, 2019) & & & \\  & Crushed stone regeneration & K016 + 000-K016 + 600 (Guanan County, 2019) & & & \\ \cline{1-1}  & Milling and repaving one layer & K027 + 200-K028 + 400 (Yangzhou City, 2018) & & & \\ \cline{1-1}  & Milling and repaving two layers & K024 + 100-K026 + 500 (Yangzhou City, 2015) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selection of typical sections of rural roads in the Jiangsu province.  
(2) Prediction model for RRQI  
Based on the road condition inspection data from 2016 to 2021, the RRQI data of typical sections of asphalt pavement and cement pavement on rural roads in Jiangsu province were fitted and analyzed. The RRQI decay curves of five different treatment levels were established, including slurry seal layer, thin layer cover layer, crushed stone regeneration, milling and resurfacing one layer, and milling and resurfacing two layers, as shown in Figures 6 and 7 below.  
Figure 4: RPCI decay curve of asphalt pavement in rural roads over time.  
Figure 5: RPCI decay curve of cement pavement damage in rural roads over time.  
Figure 6: RRQI decay curve of asphalt pavement in rural roads over time.  
Figure 7: RRQI decay curve of cement pavement in rural roads over time.