Technologies for Hazard Detection and Monitoring of Dams

1. Technologies for Hazard Detection and Monitoring of Dams Tissa Illangasekare Center for Experimental Study of Subsurface Environmental Processes, Colorado School of Mines Anura Jayasumana Department of Electrical and Computer Engineering Colorado State University

2. Teton Dam Failure Teton Dam, a 305-foot high earth fill dam across the Teton River in Madison County, southeast Idaho, failed completely and released the contents of its reservoir at 11:57 AM on June 5, 1976. Failure was initiated by a large leak near the right (northwest) abutment of the dam, about 130 feet below the crest. The dam, designed by the U.S. Bureau of Reclamation, failed just as it was being completed and filled for the first time. • On June 3, two small springs developed on the right abutment (clear water at 40-60 gpm) • On June 4, another small spring found 150 ft from toe (20 gpm) • June 5- (7:30 -8:00 am) ain leak flowing 20-30 cfs from a rock on right abutment near the toe 11:00 am- a whirlpool observed 11:30 am- embankments eroding 11:55 am the embankment fell into water and dam breached at 11:57 am 2-2.5 hours after the first seepage was observed, the dam failed releasing 251,000 act ft in 5 hours

3. What need to be monitored and how? • Need adequate time for warning • Observe parameters that are good indicators to detect failure • Warning needed for: • Possible failure of the dam • Flood risk and damage (evacuation)

4. 1. Embankment dams Pore-pressure Movement Seepage

5. 2. Gravity dams Displacement Tiltmeter H

6. 3. Arch Dams Multiple tiltmeters

7. Distributed Multi-sensor Network Monitoring Decision Making Sensor Network Monitoring • Utilize Networking and Processing Technologies to: • Achieve continuous real-time monitoring • Overcome geographic limitations

8. Example 1 Sensor Network for 3-D Transient Plume Monitoring (CSU/COSM/Sandia) Land surface S-nodes Sensor Installation wells Spill Water table 3-D Plume W-nodes Test-bed

9. Sensor Networks – Bridging Physical and Digital Worlds Low data rates High data rates Power limited Not power limited Processing limited Not processing limited Storage limited Not storage limited Motes …………………..Cameras…...…….Radars, Observatories

10. Example: CASA - Collaborative and Adaptive Sensing of the Atmosphere assimilation/ synthesis of data signal processing numerical weather prediction radars Control: what/when to sense, compute resource database sensing,meteorological utility functions policy “external” users

11. Mote-based Sensor Networks Wireless Low data rates Power limited Processing limited Storage limited

12. Ex. Seismology • 38 strong-motion seismometers in 17-story steel-frame Factor Building. • 100 free-field seismometers in UCLA campus ground at 100-m spacing http://nesl.ee.ucla.edu/tutorials/mobicom02/Deborah Estrin ¾¾¾¾ 1 km ¾¾¾¾¾¾

13. Ex. Monitoring Water Quality Source: Technology Review, July/Aug 2003

14. Distributed Multi-sensor Network Monitoring • Choice of Technologies • Modem dial-up • Dedicated phone/data line • Satellite • …. Choice of Technologies -Wired/Wireless/Hybrid -Manual/Automated …… Monitor Archive Local Monitor Sensor Network Archive • Choice of Technologies • -Server • Man in the loop (Engineer/Technician) • Control/Monitoring room • ….. Sensors Choice of Technologies -Simple communication relay -Personal Computer -Workstation - Engineer/Technician ….. • Choice of Technologies • Pressure • Tiltometers • Time-lapse photography • Real-time/Manual • …..

15. Distributed Multi-sensor Network Monitoring Monitor Archive Archive Local Monitor Sensor Network Sensors • Operational Modes: • Autonomous (Computer Based) • Monitor • Change Monitoring • Send Alert • Remotely Controlled /Manual • All information made available to remote location • Hybrid of the two