Safety Inspection for Infrastructure Laboratory
| Principal Researcher | Junkyeong Kim |
|---|---|
| Website | https://aict.snu.ac.kr/ |
| Tel | +82-31-888-9407 |
| junkyeong@snu.ac.kr |
Research Areas
- The Safety Inspection for Infrastructure Lab researches on the disaster prevention and safety of disasters and disasters that occur in cities, facilities, and structures that become more complex. We use IoT, AI, and big data to diagnose social disaster safety and research methods to solve safety-related problems such as cracks and strains of facilities and structures.
keyword
- Disaster prevention
- Infrastructure Inspection
- Structural Dynamics
- Sensor Engineering
- Information Processing Technology
Objective
- Gain safety-related values and insights from historical data and solve social safety issues
- Safety management of facilities and structures using IoT technology
- Innovation and value creation of safety diagnosis through the convergence of 4th industrial technology in the disaster prevention and safety field
Contents of research
- Jack Support Remote Load Monitoring System: A technology that detects and prevents accidents such as collapse by detecting dangerous loads at construction/demolition sites by establishing a system that wirelessly monitors the working load applied to the jack support using a Bluetooth-based load cell.
- Deteriorated PSC girder safety diagnosis method based on internal self-response scan: Technology to prevent possible accidents such as collapse by constructing an aging PSC girder tension measurement system by magnetizing only a specific position using twin coils
- Development of residual tension measurement technology of aging PSC structures: technology to prevent possible accidents such as structure collapse by performing multiple measurements of external magnetized EM and 3D GPR to diagnose tension of aging PSC structures
- Development of social disaster diagnosis technology and safety diagnosis system platform
-Research and implementation of monthly and quarterly risk visualization functions that combine past disaster history data and social data
-Research and implementation of intelligent social disaster analysis function based on social disaster big data
-Development of intelligent social disaster risk detection and safety diagnosis platform
Result & Expected effect
- Secure citizen safety and reduce social anxiety by establishing a safe diagnosis system to prevent accidents and manage them safely
- Scientifically predicting the uncertainty of the future society and data-driven digital innovation for the whole society`
