Infrastructure — bridges, tunnels, water and transport networks — is ageing while budgets are tight. A digital twin creates a live virtual replica of an asset, fed by real sensor data, so owners can monitor condition, predict problems and test interventions virtually before acting in the costly physical world.
Working principle
The twin couples three things: a geometric/semantic model (often from BIM), IoT sensors on the asset (strain, displacement, vibration, flow), and analytics that interpret the data. Live measurements update the model's state; simulation predicts behaviour under loads, weather or 'what-if' scenarios; and the insights inform maintenance and operation. Unlike a static model, the twin evolves with the asset throughout its life.
| Stage | Capability |
|---|---|
| BIM model | As-designed/as-built geometry |
| Connected twin | Live monitoring of condition |
| Predictive twin | Forecast deterioration / loads |
| Autonomous / city twin | Optimise networks, scenarios |
Key challengeScaling from a single asset to a city-scale twin means integrating many data sources and standards — interoperability, not sensing, is the limiting factor.
Applications
- Structural health monitoring of bridges and tunnels
- Smart water and energy network operation
- City-scale planning, flood and traffic scenario modelling
References & further reading
- Centre for Digital Built Britain, “The Gemini Principles,” 2018.
- Boje et al., “Towards a semantic Construction Digital Twin,” Automation in Construction, 2020.
- Lu et al., “Digital twin-enabled anomaly detection for built asset monitoring,” Automation in Construction, 2020.