AI-Powered Sensor Anomaly Detection for Major Bridges

Safety & Security

What it collects

About a measurement

Anonymized data

Structural health monitoring sensor data from the Samuel de Champlain Bridge, including readings such as strain, vibration, temperature, or deflection used to assess bridge integrity over time.

Bridge Anomaly Detection Project — GC AI Register

Run by: Housing, Infrastructure and Communities Canada (HICC)
Where: No fixed location
Kept: Not stated by the Helpful Places.
Shared with: Accountable organization

What it is for

Safety & Security

This system uses machine learning to automatically detect unusual readings in sensor data collected from major bridges managed by Housing, Infrastructure and Communities Canada, starting with the Samuel de Champlain Bridge. It is designed to help government engineers identify potential structural concerns earlier than manual review alone would allow. The system does not collect any personal information and its use is disclosed to users.

What it collects and what happens to it

Data taken in

About a measurement

Anonymized data

Structural health monitoring sensor data from the Samuel de Champlain Bridge, including readings such as strain, vibration, temperature, or deflection used to assess bridge integrity over time.

Bridge Anomaly Detection Project — GC AI Register

Processing

Anomaly Detection

Machine learning models trained on historical bridge sensor readings to identify deviations from normal operating baselines, flagging unusual patterns for engineer review.

Bridge Anomaly Detection Project — GC AI Register

What it does

Deciding (Analytical AI)

Human decides

The system classifies sensor readings as anomalous or normal, generating flags for review by GC engineers who then determine whether further inspection or action is required.

Bridge Anomaly Detection Project — GC AI Register

Outputs

A recommendation or prediction

Anonymized data

Anomaly flags and scores derived from bridge sensor data, surfaced to GC engineers as alerts or reports. These are advisory outputs — human engineers decide on any follow-up inspection or maintenance action.

Bridge Anomaly Detection Project — GC AI Register

Run by

Housing, Infrastructure and Communities Canada (HICC)

The federal department responsible for deploying and operating this machine learning system to monitor sensor data from major bridges under its jurisdiction.

Bridge Anomaly Detection Project — GC AI Register

Built by

Government of Canada

The system was developed internally by the Government of Canada, not by an external commercial vendor.

Bridge Anomaly Detection Project — GC AI Register

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization

Anomaly detection outputs and sensor data are accessible to GC employees within Housing, Infrastructure and Communities Canada who are involved in bridge monitoring and maintenance operations.

Bridge Anomaly Detection Project — GC AI Register

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.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

Right to Algorithmic TransparencyThe Government of Canada has disclosed that this system uses AI, as indicated in the official AI register. GC employees who are primary users of this system are informed of AI use. Members of the public may consult the Government of Canada's open AI register for information about how this system operates.

Risks and safeguards

Physical harmmissed anomalies or false negatives could allow structural deterioration to go undetected, potentially contributing to safety failures on major bridges.Safeguard: the system is advisory — GC engineers review all flags and make final decisions; human expertise remains the primary safety check. The system supplements rather than replaces conventional inspection regimes.