## 5 Conclusions  
In this study, the method for evaluating the status of ballasted tracks of a high-speed railway using a 2-D image analysis was improved, and the following conclusions were drawn. 1. When the background color for taking images of gravel aggregates was changed and the image analysis algorithm was improved, the aggregate segmentation rate was significantly increased. The segmentation rate of a previous method ranged from 51 to 100%, as it was considerably affected by the image shooting environment, whereas that of the improved method was 100% regardless of the image shooting environment. 2. When the image analysis was performed on the samples collected from various location of a high-speed railway, the results revealed that roundness has the highest correlation with TQI, whereas sphericity has the highest correlation with the number of maintenance works. However, aspect ratio and angularity exhibited a low correlation with other shape indices owing to over simplification of a gravel shape and the limitation of the analysis method. 3. It was discovered that the correlation can be detected when the gravel samples are collected from the surface, as well as from the middle layer of a high-speed railway for the image analysis. 4. If the abrasion of the ballast aggregates approaches to about 10%, the ballast layer would be replaced in order to enhance maintenance efficiency. Applying a 3-D image analysis method is more suitable for high-speed rail ballast than a 2-D image analysis method as explained previously; however, a 2-D image analysis method was proposed in this study for track status evaluation of the ballasted tracks of a high-speed railway, considering time, cost, and field applicability at high-speed railways. To increase the reliability of the developed method, the correlation should be examined and supplemented by comparing with the result of a 2-D image analysis method with that of a 3-D image analysis method. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No. of Cycle**} & \multicolumn{6}{c}{**Residual Weight (kg)**} & \multicolumn{3}{c}{**\% of Passing (22.4 mm)**} \\ \cline{2-7}  & **50 mm** & **40 mm** & **31.5 mm** & **22.4 mm** & **Etc.** & **Total** & \\ \hline
0 & 2.02 & 3.38 & 2.80 & 1.81 & 0.00 & 10.00 & 0.0 \\ \hline
200 & 1.08 & 3.11 & 2.54 & 2.23 & 0.95 & 9.92 & 9.6 \\ \hline
300 & 1.11 & 3.05 & 2.41 & 2.05 & 1.29 & 9.91 & 13.0 \\ \hline
500 & 0.77 & 2.43 & 2.44 & 2.51 & 1.73 & 9.87 & 17.6 \\ \hline
1000 & 0.38 & 2.30 & 2.35 & 2.41 & 2.28 & 9.72 & 23.5 \\ \hline
2000 & 0.27 & 2.30 & 2.10 & 2.31 & 2.62 & 9.60 & 27.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result of sieve analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No. of Cycle**} & \multirow{2}{*}{**Abrasion Loss (\%)**} & \multicolumn{3}{c}{**Shape Index**} \\ \cline{3-5}  & & **Roundness** & **Sphericity** & **Aspect Ratio** & **Angularity** \\ \hline
0 & 0.00 & 0.595 & 0.836 & 1.260 & 0.00209 \\ \hline
200 & 3.05 & 0.618 & 0.854 & 1.253 & 0.00176 \\ \hline
300 & 3.99 & 0.626 & 0.862 & 1.242 & 0.00176 \\ \hline
500 & 8.41 & 0.638 & 0.868 & 1.235 & 0.00166 \\ \hline
703 & 10.57 & 0.646 & 0.871 & 1.231 & 0.00160 \\ \hline
1000 & 13.73 & 0.651 & 0.874 & 1.224 & 0.00159 \\ \hline
2000 & 21.03 & 0.665 & 0.881 & 1.214 & 0.00139 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: No. of cycle, abrasion loss and shape index relationship. When the images of the field gravels were analyzed, the abrasion loss of gravels was less than 4%. However, the relationship between the abrasion loss and shape index used in this study considered the abrasion loss of up to 21%, which indicates that an accurate analysis is required for sections with a smaller abrasion loss. Thus, an additional image analysis on the low number of revolutions in the L.A. abrasion test must be conducted. Also, it is required to conduct more field tests to find out clear trend between ballast abrasion loss and TQI or maintenance. Conceptualization, Y.-T.C., B.P.