# Airport pavement Maintenance Decision-Making System with Condition Cases Optimization
## 3 Case Study  
We compared the average SL at the end of the analysis period with the DN scenario using actual pavement evaluation data from 164 homogeneous flexible pavement sections, including runways, aprons, and taxiways. Table 7 provides information on airport-specific equivalent annual takeoff traffic, traffic groups, branch configurations, \(\lambda\) weights, and the number of sections corresponding to CC I, II, and III. In the case of Airport C, there were two sections falling under CC I, while for CC II, Airport A had three sections, and Airport C had fifteen sections. Annual traffic was converted using the B737-900 ER aircraft, and \(\lambda\) values were determined based on the runway numbers and traffic group, with respective values of 0.6, 0.2, 0.4, and 0.7.  
When analyzed under the condition of sufficient budget, the annual average SL over the years is as shown in Figure 5. For DN, the average SL decreased steadily over time, starting from an initial average of 86 points and reaching approximately 76 points after 5 years. When the NO method was applied, the SL increased to 87 points in the subsequent years after the analysis, gradually declining to about 80 points. Considering the domestic airport pavement, which is generally well-maintained, it seems reasonable that there was little difference in the numerical values.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Input Value**} & \multicolumn{3}{c}{**Total B/C Difference (\%)**} & \multicolumn{3}{c}{**SL at the End of the Analysis**} \\ \cline{3-6}  & & **NO** & **CCO** & **DN** & **NO** & **CCO** \\ \hline \multirow{2}{*}{Life extension effect} & \(-20\)\% & 99.41 & 108.40 & 100.00 & 99.03 & 99.20 \\  & **0\%** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** \\  & 20\% & 100.51 & 72.94 & 100.00 & 100.74 & 100.42 \\  & \(-20\)\% & 125.04 & 134.85 & 100.00 & 100.53 & 100.21 \\ M\&R cost & **0\%** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** \\  & 20\% & 83.25 & 8.48 & 100.00 & 98.67 & 99.20 \\  & 40 & 231.03 & 458.69 & 100.00 & 96.80 & 96.70 \\ SL threshold & **65** & **100.00** & **100.00** & **100.00** & **100.00** & **100.00** \\  & 80 & 34.09 & 0.53 & 100.00 & 100.35 & 100.20 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Airport** & **A** & **B** & **C** & **D** \\ \hline Annual Departures & 45,832 & 145 & 681 & 35,192 \\ Number of Sections per & Runway: 42 & & & Runway: 64 \\ Branch & Taxway: 10 & Runway: 10 & Runway: 25 & Taiwanway: 6 \\ \(\lambda\) & 0.6 & 0.2 & 0.4 & 0.7 \\ CC I Sections & 0 & 0 & 2 & 0 \\ CC II sections & 3 & 0 & 15 & 0 \\ CC III sections & 56 & 10 & 8 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Information about sections used for the case study.  
To examine the effect of the budget--one of the advantages of the decision-making method presented in this paper--the total B/C was evaluated while incrementally increasing the budget from 0 to 2400 in increments of 400 (as shown in Figure 6). Initially, for the CC I, where immediate maintenance is required, the B/C was around 1. Afterward, for CC II, it increased to approximately 50 after a budget of 200. However, for CC III, starting from 800, the B/C rapidly increased to 10,000 or more. This steep increase likely occurred because high-scoring CC III segments, which were initially in good condition, began to be included in the maintenance targets, leading to a rapid increase in the B/C.