Researchers Use AI to Prevent Lock Dam Failures
A new research project uses sensor data and AI to identify structural damage in navigation locks before failures occur.
Updated on Oct. 16, 2026 in Safety
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As of October 1, 2026, a research team is conducting a one-year study to develop structural health monitoring methods for navigation locks. The project utilizes sensor data from The Dalles Locks and Dam to build computerized models for identifying potential gate stresses.
Why it matters
The research aims to proactively identify lock gate damage, allowing for repairs to be scheduled before major failures occur. This approach seeks to prevent the navigation traffic delays and closures that have followed past structural issues at various dam sites.
The research team is utilizing data from sensors and strain gauges to model gate integrity. Structural problems previously impacted the Demopolis, Wilson, and Lockport Lock and Dam facilities between January 2024 and January 2025.
The players
Dr. Trent Schreiber
He leads the research team at the Construction Engineering Research Laboratory focused on navigation lock gate integrity.
Construction Engineering Research Laboratory
This facility conducts research on behalf of the Department of War to improve infrastructure engineering and maintenance.
The details
Led by Dr. Trent Schreiber of the Construction Engineering Research Laboratory, the three-person team employs computerized simulations to analyze stress patterns on lock miter gates. These models are designed to identify structural weaknesses in gates that lack comprehensive sensor installations.
Timeline
Structural problems occurred at Demopolis Lock and Dam in January 2024.
Structural problems occurred at Wilson Locks and Dam in September 2024.
Structural problems occurred at Lockport Lock and Dam in January 2025.
The research study began on October 1, 2026.
The one-year research study is scheduled to conclude in October 2027.
Seasonal Patterns
This research follows a pattern set by the 2024-2025 structural failures at Demopolis, Wilson, and Lockport locks by developing tools to mitigate similar future disruptions. It represents a shift from reactive repairs to predictive maintenance for critical inland waterway infrastructure.
The potential implementation of these monitoring methods could reduce unplanned lock closures that currently delay commercial and recreational navigation. This research may eventually provide a lower-cost alternative to expensive, permanent underwater sensor installations for waterway operators.
The takeaway
Proactive monitoring of lock gate stress patterns is becoming a critical tool for maintaining the reliability of inland waterways. Implementing AI-driven maintenance schedules could effectively minimize the economic impact caused by unexpected navigation delays.
Further reading
For additional context on infrastructure protection, visit the Safety section.
Source note: This article includes information reported by The Waterways Journal.
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