AI-Based Damage Risk Prediction Model Development Using Urban Heat Transport Pipeline Attribute Information  
[PERSON]  
Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Republic of Korea; [EMAIL_ADDRESS] (S.L. ); [EMAIL_ADDRESS] (J.K.); gold [EMAIL_ADDRESS] (J.K.)  
[PERSON]  
Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Republic of Korea; [EMAIL_ADDRESS] (S.L. ); [EMAIL_ADDRESS] (J.K.); gold [EMAIL_ADDRESS] (J.K.)  
[PERSON]  
Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Republic of Korea; [EMAIL_ADDRESS] (S.L. ); [EMAIL_ADDRESS] (J.K.)  
Jmkang  
Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Republic of Korea; [EMAIL_ADDRESS] (S.L. ); imkang@kict  
Because damage to, and leakage from, heat transport pipelines may cause the loss of thermal energy and safety accidents, studies have been conducted on diagnosis and prediction technologies. Systems capable of diagnosing leakage have been developed based on various image and sensor data, including infrared thermal imaging cameras, acoustic sensors, and distributed temperature measurements [6; 7]. In addition, effective heat transport pipeline leak detection technologies have been developed by analyzing changes in data, such as the pressure and flow rate of pipelines, through simulation models [8]. To predict damage to heat transport pipelines, it is important to select the factors that affect damage and conduct systematic analysis. Heat transport pipelines with a high number of damage cases were selected through statistical analysis, and their characteristics were analyzed. In addition, aging, pressure, temperature, terrain, and installation conditions were reported as major factors that cause damage [9]. In addition, a statistically based damage risk prediction model was developed using operational data from heat transport pipelines currently in service in the Republic of Korea [10]. The deterioration and aging mechanisms of pipelines were analyzed, and AI technique application methods were proposed to predict the service life of heat transport pipelines using factors that cause damage [11]. Studies have also utilized sensor data--such as temperature, pressure, flow rate, and vibration--collected from heat transport pipelines to predict potential anomalies through AI-based approaches [12]. As described, studies have been conducted to predict damage to heat transport pipelines and prevent leakage using various techniques. However, owing to the nature of urban heat transport pipelines, it is difficult to secure a large amount of data. In addition, it is difficult to develop damage prediction models using AI techniques because data on damage to heat transport pipelines represent a very small proportion. Owing to these constraints, prior research has been largely confined to AI-based anomaly detection using installed sensor data and statistical analyses based on limited attribute information. To overcome these limitations and enhance maintenance decision-making, this study developed a machine learning-based damage probability prediction model by segmenting the heat transport pipelines currently operated in the Republic of Korea into analytical units, using their spatial and attribute data. In this study, the spatial and attribute information of heat transport pipelines and their damage history data were collected. Based on the spatial and attribute information, the pipelines were divided into basic units according to certain standards. Heat transport pipeline damage probability, according to the attribute information, was analyzed using the data for each basic unit, and factors that affect data characteristics and damage were identified through correlation analysis.