# Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements
## 3 Results and Discussion
### Application of Long-Term Maintenance Decision-Making Optimization Method
#### 3.3.2 Maintenance Decision-Making Solution  
According to the \(PCI\) predictive model (Equations (1)-(6)) and the model parameters in Tables 3 and 4, the average \(PCI\) of the terminal runway and middle runway will be 88.4 and 83.9 at the end of stage 5, respectively, if no maintenance has been implemented during the whole period. Due to \(PCI_{\text{min}}\), some maintenance must be implemented in advance. Based on the optimization IDP algorithm, the non-inferior solutions from stages 5 to 1 are shown in Figure 7. Figure 7 indicates that the optimal solution for each stage is \"no maintenance.\" However, considering the constraint \(PCI_{\text{min}}=90\), preventive maintenance should be implemented in the early stages when it is more economical.  
Table 6 lists the benefit-cost rates of various maintenance-decision combinations in stage 1. Maintenance decision combination 2 (\(p_{k=1}=\begin{bmatrix}0&0&0&0&0&0&0&2\end{bmatrix}\)), which has the maximum benefit-cost rate, is the optimal solution, but the increase in \(\overline{PCI}_{k=5}\) is inadequate to meet the requirement of \(PCI_{\text{min}}\). Thus, maintenance is still required in stage 2, and the optimal decision combination is \(p_{k=2}=\begin{bmatrix}0&0&0&0&0&1&1&0\end{bmatrix}\). In sum, the optimal maintenance plan for Sunan Shuofang Airport is treating region \(8^{\#}\) with fog seal in 2013 and treating regions \(6^{\#}\) and \(7^{\#}\) with crack filling in 2014.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & \(P_{\text{k=1}}\) & \(C_{\text{k=1}}\)**(5)** & **Increase in \(\overline{PCI}_{\text{k=5}}\)** & **Benefit–Cost Rate (\(\times 10^{-5}\))** \\ \hline
1 & [0, 0, 0, 0, 0, 0, 2] & 24,409 & 0.97 & 3.97 \\
2 & [0, 0, 0, 0, 0, 0, 0, 3] & 27,460 & 1.11 & 4.04 \\
3 & [0, 0, 0, 0, 0, 0, 5] & 33,285 & 1.28 & 3.85 \\
4 & [0, 0, 0, 0, 0, 2, 2] & 48,818 & 1.95 & 3.99 \\
5 & [0, 0, 0, 0, 0, 2, 3] & 51,869 & 2.08 & 4.01 \\
6 & [0, 0, 0, 0, 0, 0, 3, 3] & 54,920 & 2.21 & 4.02 \\
7 & [0, 0, 0, 0, 0, 0, 2, 5] & 57,694 & 2.25 & 3.90 \\
8 & [0, 0, 0, 0, 0, 0, 3, 5] & 60,745 & 2.39 & 3.93 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Benefit-cost rates of \(p_{k=1}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Maintenance Technology** & **Unit Cost** & **Maintenance Length/Area** & **Total Costs (\(\Delta c_{k=5,ni}\))** \\ \hline Crack filling & $2.4/m & 11,000 m & $26,400 \\ Fog seal & $3.3/m\({}^{2}\) & 9000 m\({}^{2}\) & $29,700 \\ Hot-mix recycled AC & $4.0/m\({}^{2}\) & 9000 m\({}^{2}\) & $36,000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cost of preventive maintenance in stage 5.  
Figure 7: Non-inferior solutions of each stage.  
The average _PCI_ value of composite pavements and the maintenance costs during the whole period are shown in Figure 8. Based on the abovementioned decision-making optimization method, this study's five-year preventive maintenance plan not only sufficiently meets the demand for pavement performance but also spends maintenance funds properly without exceeding the budget.