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This session addresses a core limitation in bridge inspection, manual checks that cannot capture continuous structural change. Dr. Venkata Dilip Kumar Pasupuleti (Head, CSIS, Mahindra University) covers how vision based monitoring, vibration based monitoring, and AI driven analysis each contribute to a fuller picture of bridge health.
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Description
Most Structural Health Monitoring approaches treat visual inspection and sensor based monitoring as separate methods. This session covers how each technology contributes to a fuller picture of bridge health.
Covered in this session
- Vision based monitoring, drones, cameras, and computer vision to detect cracks, corrosion, and surface deterioration
- Vibration based monitoring, structural response data to track changes in dynamic behavior
- Advanced data analysis, 3D structural models, natural frequencies, damping ratios, mode shapes
- Vision and vibration fusion, combining both data types for more reliable damage assessment
- AI driven monitoring, machine learning to separate structural change from environmental noise
- Digital twins and decision support, connecting monitoring data to predictive maintenance planning
Key Takeaways
How vision based and vibration based monitoring each capture different types of structural information, and where each falls short on its own
How AI driven data fusion combines both data types into a single, more reliable damage assessment
How digital twins connect monitoring data to decision making for predictive maintenance planning
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