Summary
This Case Study covers a 68-metre high concrete gravity dam built in 1967, with over 120,000 people at risk downstream. A dam safety review had flagged three critical deficiencies — poor foundation drainage monitoring, no automated seepage detection, and no real-time structural deformation tracking. OctaSense upgraded the instrumentation system and deployed an AI-driven load-response correlation model that learns normal seasonal behaviour and flags any deviation. Within 4 months, it detected a gradual increase in foundation seepage that wasn't proportional to reservoir levels — an early warning of potential foundation piping that manual quarterly readings had completely missed.
Background & Context
The global inventory of aging large dams represents one of the most significant and underappreciated infrastructure risk challenges of the 21st century. Thousands of dams constructed in the 1950s through 1980s are now approaching or exceeding their original design life, operating under hydraulic and seismic loads that were not fully anticipated in their design. Regulatory frameworks for dam safety are tightening worldwide, requiring dam owners to demonstrate continuous monitoring and documented risk management as a condition of operating licence.
This case study involves a 68-metre high concrete gravity dam constructed in 1967, impounding a reservoir with a population at risk exceeding 120,000 people downstream. A recent dam safety review had identified three deficiencies: inadequate monitoring of foundation drainage efficiency; no automated detection of unusual seepage through the dam body; and no real-time structural deformation monitoring. The dam owner was required to address all three deficiencies within 18 months.
Sensor Deployment
Key Outcomes & Results
OctaSense alert system integrated with the dam emergency action plan — automated notification to downstream emergency services
40 hours/month eliminated
Manual report time saved
Deployment Snapshot
Location
California, USA