## 5 Discussion  
In this study, attribute information and damage history data of heat transport pipelines were collected to analyze the damage probability based on these attributes and to develop a risk model. The pipelines were divided into basic units following certain standards.  
Figure 8: Analysis of important factors using models.  
Figure 9: Heat transport pipeline damage probability risk map.  
Damage probability according to attribute information was analyzed using the segmented pipeline data, and an AI-based model for predicting heat transport pipeline damage probability was developed by selecting statistically significant factors.  
It was found that damage to heat transport pipelines is influenced by the period of use, operator, pipe function, pipe diameter, and sensor wire condition. As the period of use increased, damage probability tended to rise, owing to aging. This likely results from aging damaging the insulation material that protects the steel pipes and prevents heat loss; corrosion then occurs from leakage of internal hot water and infiltration of external groundwater through damaged areas [43]. Differences in sensor wire condition also affected damage probability for the same reason. Previous studies reported that the service life of pipelines using the same steel pipes as heat transport pipelines, including water supply systems, is at least approximately 30 years, despite variations due to internal and external conditions. Damage probability analysis of the target area's heat transport pipeline data similarly showed high damage probability for pipes used over 30 years [18; 19]. Damage probability also varied significantly by operator, likely due to differences in management and maintenance guidelines and soil moisture content affected by topographical characteristics (altitude and slope) [44; 45; 46; 47]. Heat transport pipelines carry hot water under high temperature and pressure, causing damage primarily to supply pipes. Pipe diameter was not closely related to damage, although mid-sized pipelines tended to exhibit lower damage probability.  
An AI-based model predicting heat transport pipeline damage probability was developed. Owing to an imbalance between input data (attribute information) and output data (damage status), the output data were reinforced with pipelines used over 20 years, because damage probability sharply increased with age. Applying this reinforced dataset to RF, XGB, and LGBM models produced a model with approximately 74% accuracy and an F2-score of approximately 0.804. This result demonstrates higher reliability compared to previous studies that relied on statistical models using single factors such as pipeline age or operator, as the proposed model incorporates multiple influencing factors in combination [10]. The high F2-score indicates good classification of damaged pipelines. In particular, the model demonstrates strong detection performance for damaged pipelines, distinguishing itself from prior studies that focused heavily on accuracy alone [12]. When including pipes used over 30 years, model accuracy and AUC improved compared to those including pipes over 20 years. The primary goal was to predict damaged pipelines; therefore, the F2-score was the key evaluation metric, with accuracy and AUC serving as auxiliary indicators to assess reliability. A risk map visualizing pipeline damage risk was created using the model, enabling prioritized maintenance and proactive management of high-risk pipes to prevent accidents.  
The proposed model predicted risks using limited factors due to data collection challenges. Future research should aim to develop a more reliable model by incorporating diverse internal and external factors, including various pipeline attribute information, geotechnical data, auxiliary facilities of heat transport pipelines, and nearby excavation activities.