## 4 Application of Pavement Management Platform and Validation with Practical Cases
### Practical Case Inspection and Validation  
* Validation Case Range This study selects \"Provincial Highway No. 2 from 16K+000 to 19K+000, inner and outer lanes in the forward direction\", totaling 60 pavement units, as the validation section.
* Application and Validation Methods The maintenance units of provincial highways develop maintenance strategies for the validation section based on the traditional decision-making method described in Section 1.1, dividing the 60 pavement units into \"Maintenance Sections\" and \"Observation Sections\". Using the inspection indicator grading developed in this study and the grading thresholds in Table 14, the 60 pavement units are classified into \"Maintenance Sections\", \"Continuous Monitoring Sections\", and \"Good Condition Sections\". Considering that the traditional decision-making method only results in two decision outcomes, for comparative analysis, the \"Continuous Monitoring Sections\" and \"Good Condition Sections\" are classified as \"Observation Sections\". The accuracy of the decision results from the two methods is calculated using the example in Equation (4). Validation Accuracy = 100% \(\times\) (Number of Accurate Decisions/Total Number of Sections) where: Number of Accurate Decisions = Number of sections where both decisions are \"Maintenance Sections\" or \"Observation Sections\". Total Number of Sections = 60 pavement units from 16K+000 to 19K+000 in the forward direction
* Inspection Results The crack rate, rutting depth, and IRI of 60 pavement units on \"Provincial Highway No. 2 from 16K+000 to 19K+000, inner and outer lanes in the forward direction\" were inspected. The inspection results are graded based on the method in Section 3.3. The grading results for the crack rate, rutting depth, and IRI are shown in Figures 10-12, respectively.  
Figure 9: Integration of maintenance decisions into pavement management platform.  
* Validation Results  
Subsequently, the maintenance decisions for 60 pavement units on \"Provincial Highway No. 2 from 16K+000 to 19K+000, inner and outer lanes in the forward direction\" are validated:  
Figure 11: Rutting depth grading results. (**a**) Inner lane; (**b**) Outer lane.  
Figure 12: IRI grading results. (**a**) Inner lane; (**b**) Outer lane.  
Figure 10: Crack rate grading results. (**a**) Inner lane; (**b**) Outer lane.  
* Based on the inspection results of this study, the 60 pavement units are graded into \"Maintenance Sections\", \"Continuous Monitoring Sections\", and \"Good Condition Sections\", as shown in Figure 13a.
* The provincial highway maintenance units make maintenance decisions based on the traditional method, dividing the 60 pavement units into \"Maintenance Sections\" and \"Observation Sections\", as shown in Figure 13b.
* The \"Continuous Monitoring Sections\" and \"Good Condition Sections\" from this study are both classified as \"Observation Sections\", and the validation accuracy of maintenance decisions is analyzed using Equation (4).
* The calculation results are shown in Table 16. The final validation accuracy is 70%, indicating that the decision results of this study mostly align with the actual maintenance decisions of the provincial highway maintenance units. This demonstrates that the inspection and decision-making methods developed in this study meet the needs of actual maintenance units and can replace traditional methods for more efficient decision-making.  
\begin{table}
\begin{tabular}{c c c} \hline
**Validation Section** & **Total Number of Sections** & **Number of Accurate Decisions** & **Validation Accuracy** \\ \hline Provincial Highway No. 2 & & & \\ from 16K+000 to 19K+000, & & & \\ inner and outer lanes in the & 60 & 42 & 70\% \\ forward direction & & & \\ \hline \end{tabular}
\end{table}
Table 16: Practical application validation results.  
Figure 13: Decision validation analysis. (**a**) Decision results of this study; (**b**) Actual maintenance sections by provincial highway maintenance units.