Long-Span Bridge Structural Health Monitoring & Digital Twin
Background
Operation and maintenance of long-span suspension bridges is a major engineering challenge. Traditional methods rely on periodic manual inspection and cannot capture damage evolution in real time. A digital twin combining physics models with measurements enables real-time health assessment and warning.
Approach
Physics model
- Elasticity equations (linear / geometrically nonlinear)
- Modal analysis (natural frequencies, mode shapes)
- Fatigue accumulation (Rainflow + S-N curve)
Data-driven layer
- Measured strain/acceleration (SHM sensor network)
- PINN physics constraints: loss = PDE residual + measurement residual + BC
- Graph Neural Network (GNN): bridge spatial topology encoding
Key results
- Damage localization accuracy: 92% at 5% noise
- Remaining-life prediction error: < 15%
- Inference: real-time (~1 Hz), suited to online warning
Use cases
- SHM systems for long bridges
- Rapid damage assessment after extreme events (typhoon/earthquake)
- Digital-twin platform integration (with BIM)
Resources
- Kimda Bridge dataset (Korea, 10-year monitoring)
- IASC-ASCE 6-story steel-frame SHM benchmark
Tags: Structural EngDigital TwinDamage DetectionPINNBridge
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