Facility management and disaster response focus on critical national infrastructure and public safety, requiring significant time for the development and demonstration of monitoring and informatization technologies. This is primarily because most management information is classified, and on-site conditions are diverse and vulnerable. Issues identified through demonstration projects can be addressed to refine and generalize the technology for broader use. Notably, the components of monitoring informatization technology—such as the Internet of Things (IoT), Artificial Intelligence (AI), Geographic Information Systems (GIS), and Building Information Modeling (BIM)—can be developed and tested individually. However, integrating and operating these technologies as a unified monitoring system in real-world environments remains highly challenging due to limited conditions and resources.
Key challenges in monitoring technology demonstration projects can be summarized into three main points:
Low communication quality in wireless monitoring systems for large structures, such as bridges, necessitated the enhancement of wireless communication modules and related monitoring systems.
Issues such as data gaps and noise required effective data preprocessing and analysis techniques.
It was essential to visualize the acquired data using BIM and GIS according to the characteristics of the structures or spaces, rather than relying solely on conventional dashboards. These requirements emphasized the need for advanced technology capable of widespread application.
For efficient and reliable facility monitoring, LoRa communication modules were utilized for IoT device communication, with a linear topology applied for spatial deployment. Traditional bridge monitoring systems used WLAN operating in the 2.4 GHz frequency band, which offered high transmission speeds but consumed more power and had a shorter communication range. In contrast, LoRa, a type of Low-Power Wide Area Network (LPWAN) operating at 850 MHz, provided slower transmission speeds of 30–50 kbps. However, it was highly energy-efficient and supported an extensive communication range of approximately 2 km.
One major limitation in monitoring informatization technologies involved the reliability and standardization of data collected from IoT sensors installed on various infrastructure elements such as bridges and road surfaces. This study focused on preprocessing urban data into formats suitable for AI-based learning models and deriving decision-making strategies to enable practical application by relevant departments in Seoul. The developed prediction model aimed to utilize monitoring data collected through all-in-one sensors and infrared road surface temperature sensors installed at various sites. The overall model performance demonstrated excellent results.
However, minimizing errors in the data collection stage is critical for the sustainable operation of IoT sensor-based monitoring systems. Additionally, in Seoul, where extensive meteorological data is available from various sources—such as Seoul Urban Data (S･DoT), the Korea Meteorological Administration, and on-site sensors—further research is needed on methods for data interpolation in missing sections and integrating urban data for diverse applications.