AI-Assisted Bridge Health Monitoring for Infrastructure Maintenance
Safety & Security · Planning & Decision-making
What it collects
- Sensor readings and physical condition measurements collected from a highway bridge in Manitoba, including structural monitoring data used to assess infrastructure state over time. No personal information is involved.
- Location and structural data about a specific highway bridge in Manitoba, used to study conditions and develop generalizable bridge health prediction methods.
- Run by
- National Research Council Canada (NRC)
- Where
- No fixed location
- Kept
- Not stated by the Helpful Places.
- Shared with
- Accountable organization
- Your copy
- You cannot see the data it holds about you. What you can do
What it is for
This system uses artificial intelligence to monitor the structural health of highway bridges and support government decisions about maintenance, renewal, or replacement. Developed by the National Research Council of Canada in partnership with federal and academic partners, it analyses sensor and condition data from a Manitoba bridge to predict how long infrastructure will remain safe. The system is currently in development and is intended to help public officials allocate maintenance investment more effectively, with no personal information collected or processed.
What it collects and what happens to it
Data taken in
- Sensor readings and physical condition measurements collected from a highway bridge in Manitoba, including structural monitoring data used to assess infrastructure state over time. No personal information is involved.
- Location and structural data about a specific highway bridge in Manitoba, used to study conditions and develop generalizable bridge health prediction methods.
Processing
- Machine learning models that classify bridge health status and predict remaining useful life or maintenance needs, trained on structural monitoring and historical condition data.
What it does
- The AI predicts bridge health and scores infrastructure condition; human government decision-makers use these predictions to determine actual maintenance or investment actions.
Outputs
- AI-generated bridge health predictions and maintenance investment recommendations provided to government officials as advisory outputs; no binding decisions are made automatically by the system.
Run by
- The National Research Council of Canada leads the AI for Logistics program and is accountable for developing and deploying this bridge health monitoring AI system, in partnership with Housing, Infrastructure and Communities Canada, Esri Canada, and the University of Manitoba.
Built by
- The Government of Canada, through the NRC AI for Logistics program, developed the AI system in collaboration with Housing, Infrastructure and Communities Canada, Esri Canada, and the University of Manitoba.
Kept for
Not stated by the Helpful Places.
Shared with
- Bridge condition monitoring data and AI outputs are available to the National Research Council Canada and partner government agencies (Housing, Infrastructure and Communities Canada) involved in the project.
- As an individual citizen, you cannot access the bridge sensor data or AI prediction outputs. The system's outputs are intended for use by government employees involved in infrastructure maintenance planning, not the general public.
Stored
Not stated by the Helpful Places.
How to read the colours
Can it identify you?
- Anonymized data
- Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
- Pseudonymous data
- Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
- Identifiable data
- The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.
Who completes the loop?
- Human decides
- This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
- Human executes
- This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
- Autonomous
- This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.
Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.
- AI registerGovernment of Canada AI Register — Enhance Transportation Infrastructure Resiliency through Bridge Monitoring (2526-NRC-CNRC-004)National Research Council Canada, AI and Data Commissioner Register, record 2526-NRC-CNRC-004.
- AI registerGovernment of Canada AI Register — 2526-NRC-CNRC-004
- AI registerGovernment of Canada AI Register — 2526-NRC-CNRC-004
- Register entryPublished by the Helpful Places. Reference a3c73f08. This disclosure was drafted with AI assistance.Schema: ai@2026-05-06-beta
What you can do
Ask about this system
Questions go to the Helpful Places, not the vendor.
Your rights
- Right to Algorithmic TransparencyThis system is publicly disclosed on the Government of Canada's AI Register. Citizens and stakeholders may consult the register entry for information about the system's purpose and development. The system processes no personal information and outputs are directed to government officials rather than members of the public directly.
Risks and safeguards
- Physical harmInaccurate bridge health predictions could lead to missed maintenance needs, potentially contributing to structural failure and physical harm.Safeguard: The system is intended as decision support for human experts, not as a replacement for professional engineering judgment. Government engineers and officials review all outputs before investment or maintenance decisions are made. The system is currently in development and subject to validation before operational deployment.