## 5 Conclusions  
This study proposed a risk assessment method using data from an integrated underground space map. It involved analyzing the causes of road subsidence, pipeline integrity, and the correlation between the surrounding environment and road subsidence to assess the risk of road subsidence caused by sewer pipes. The main findings are summarized as follows. 1. The specifications of sewer pipes were analyzed using a sewer network map to calculate the risk of road subsidence around sewer pipes. Except for buried depth, items such as pipeline age (installation year), pipe diameter, type of pipe, burial depth, and removal method were related to road subsidence. In addition, the current status of surrounding roads and maintenance history were added for the analysis. 2. As a result of logistic regression analysis, the following equation was obtained: Logit = 0.544 \(\times\) pipe type \(-\) 0.132 \(\times\) drainage method \(-\) 0.772 \(\times\) installation year \(-\) 0.364 \(\times\) pipe diameter \(-\) 1.251 \(\times\) surrounding road \(-\) 3.419 \(\times\) maintenance \(+\) 2.724. As a result of the AHP analysis, the weights were in the following order: installation year \(\rightarrow\) pipe type \(\rightarrow\) maintenance history \(\rightarrow\) surrounding ground \(\rightarrow\) drainage method \(\rightarrow\) pipe diameter. 3. Logistic regression and AHP analysis were conducted to calculate the weights for each item. The probability technique for predicting ground subsidence had an accuracy rate of 60%. As a result of assessing the conditions of sewer pipes in the pilot area using the AHP analysis, most of them were found to have the lowest grade (F). The results of evaluating the conditions where actual road subsidence occurred showed \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline Length (m) & 5853 & 16,448 & 36,518 & 75,121 & 71,124 & 236,107 \\ Ratio (\%) & 1.33 & 3.73 & 8.28 & 17.03 & 16.12 & 53.52 \\ \hline \end{tabular}
\end{table} 
Table 11: Result of sewer condition evaluation in the pilot plant. Figure 6: Rate of subsidence risk grade near the subsidence area. that over 80% were classified as E and F grades, indicating danger. This finding suggests that the equations and weights proposed in the AHP analysis were valid. This study analyzed the factors influencing road subsidence and their risk according to the sewer pipe attribute data using logistic regression and AHP analysis. Ground subsidence is usually caused by a combination of factors (damage to water pipes, excavation work, etc.). However, it is practically impossible to collect data on every individual factor. Therefore, this study conducted the AHP analysis to intuitively derive factors that influence road subsidence focusing on sewer pipelines. Additionally, logistic regression analysis was performed considering the characteristics of the data obtained. Analyzing the factors affecting road subsidence and the aging of sewer pipes will improve the accuracy of calculating sewer pipe maintenance priorities and road cave-in risks. However, the AHP analysis used in this study has a problem that may include many subjective opinions of experts. Hence, it can be inferred that measurable variables have the potential to yield outcomes of substantial reliability when the factors influencing them are assessed through mathematical methodologies, such as statistical analysis and machine learning. Furthermore, this study focused on acquiring sewage pipeline data from a specific urban area in the Republic of Korea. In the future, it is also expected that highly reliable results will be obtained by analyzing the factors and risks of road subsidence using water pipelines, excavation frequency, and subway data. Conceptualization, J.K. ([PERSON]) and J.K. ([PERSON]); AHP analysis and survey, S.L. and J.K.; writing--original draft preparation, S.L. and J.K. ([PERSON]); writing--review and editing, J.K. ([PERSON]) and J.K. ([PERSON]).