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Digital Twin for bridge maintenance based on Photogrammetry and Deep Learning

Drone imagery builds a 3D model of the bridge, deep learning finds the defects, and each defect is placed exactly where it sits on the structure.

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This animation explains the method. It is not real output.

The problem

Traditional bridge inspection depends on scaffolding, lane closures and scattered reports. Comparing a crack between two inspections is hard, and repair priorities are often set on partial information.

Capabilities

  • 3D model of the full structure
  • Detection of cracks, spalling, corrosion and leakage
  • Exact position of each defect on the model
  • Condition comparison between inspection rounds
  • Component condition scores and repair priorities
  • Inspection reports ready to print and share

Technical specifications are set for each project.

How it works

  1. Inspection flight

    A drone follows a planned route and photographs every face of the bridge.

  2. 3D reconstruction

    Photogrammetry turns the images into a textured 3D model.

  3. Defect detection

    Deep networks find cracks, spalling, corrosion and leakage.

  4. Logging and priorities

    Defects are logged on the twin and compared across inspection rounds.

Where it's used

  • Highway and urban bridges
  • Railway bridges
  • Overpasses and underpasses
  • Other concrete and steel structures

Related services and products

Have a problem that needs solving? We'll test the solution on real data.

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