Technologies for Hazard Detection and Monitoring of

DamsTissa Illangasekare

Center for Experimental Study of Subsurface Environmental Processes, Colorado School of Mines

Anura Jayasumana

Department of Electrical and Computer Engineering

Colorado State University

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.

Teton Dam Failure

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 observed11:30 am- embankments eroding11:55 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 ftin 5 hours

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)

Seepage

MovementPore-pressure

1. Embankment dams

2. Gravity dams

Tiltmeter

H

Displacement

3. Arch Dams

Multiple tiltmeters

d1

d1 d2 d3

d1 d2

d1 d2 d3 d4

SensorNetwork

SensorNetwork

Distributed Multi-sensor Network Monitoring

Monitoring

Decision Making

Utilize Networking and Processing Technologies to:

Achieve continuous real-time monitoring Overcome geographic limitations

Sensor Network for 3-D Transient Plume Monitoring (CSU/COSM/Sandia)

Spill

3-D Plume

SensorInstallationwells

Water table

Land surface

S-nodes

W-nodes

Example 1

Test-bed

Sensor Networks – Bridging Physical and Digital Worlds

Motes …………………..Cameras…...…….Radars, Observatories

Low data rates High data ratesPower limited Not power limitedProcessing limited Not processing limitedStorage limited Not storage limited

Example: CASA - Collaborative and Adaptive Sensing of the Atmosphere

assimilation/synthesis of

data

numericalweather

prediction

resource databasesensing,meteorological

utility functions

Control:what/when to sense,compute

policy

“external” users

signalprocessing

radars

Mote-based Sensor Networks

WirelessLow data ratesPower limitedProcessing limitedStorage limited

CPU 8-bit, 4MHZ

Storage 8kB Flash (Instructions)

512B RAM, 512B EEPROM

Communication 10kBps over 916 MHz radio

Operating System Tiny OS (3500B)

Available code space

4500 B

Ex. Seismology

1 km

• 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

Source: Technology Review, July/Aug 2003

Ex. Monitoring Water Quality

Distributed Multi-sensor Network Monitoring

Local Monitor

Choice of Technologies-Wired/Wireless/Hybrid-Manual/Automated……

SensorNetwork

SensorNetwork

Choice of Technologies-Simple communication relay-Personal Computer-Workstation- Engineer/Technician…..

Choice of Technologies-Modem dial-up-Dedicated phone/data line-Satellite-….

Monitor

Archive

ArchiveSensors Choice of Technologies-Server-Man in the loop (Engineer/Technician)-Control/Monitoring room…..

Choice of Technologies-Pressure-Tiltometers-Time-lapse photography-Real-time/Manual…..

Distributed Multi-sensor Network Monitoring

Local Monitor

SensorNetwork

SensorNetwork

Monitor

ArchiveArchive

SensorsOperational Modes:-Autonomous (Computer Based)

-Monitor-Change Monitoring -Send Alert

-Remotely Controlled /Manual-All information made available to remote location

-Hybrid of the two