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This system combines AI-based ground movement analysis with real-time data from IoT sensors, enabling more precise ground subsidence prediction and detection.
Accidents caused by ground subsidence have been occurring frequently recently. Existing ground safety inspection methods primarily rely on expert experience or are limited to periodic inspections conducted at set intervals, making real-time response difficult. This system combines AI-based ground movement analysis with real-time data from IoT sensors, enabling more precise ground subsidence prediction and detection. This enhances safety during construction processes and strengthens the structural stability of buildings.
This system is designed to detect ground subsidence early during building construction and preemptively eliminate risk factors.
Implementing this system prevents ground subsidence accidents during construction and ensures the long-term safety of structures. Furthermore, real-time monitoring enables construction personnel to respond more swiftly, minimizing economic and social damage.
| Before (AS-IS) | After (TO-BE) |
|---|---|
| Currently, inspections are conducted at small-scale construction sites before excavation, after excavation is completed, after excavation demolition, and when abnormalities occur; however, a predictive system is absent. | AI-based ground subsidence prediction and prevention is possible using IoT boundary point data in areas not subject to underground safety surveys. |
Launch year: 2024
Case study courtesy of the National Information Society Agency, Republic of Korea, Apolitical's Content Partner. Sourced from Use Cases of Public AI Service, Vol.1, https://eng.nia.or.kr/site/nia_eng/ex/bbs/View.do?cbIdx=31975&bcIdx=28997&parentSeq=29039
In partnership with
National Information Society Agency (NIA)
In partnership with
National Information Society Agency (NIA)





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