Summary of Asset Management, Condition Monitoring and Digital Twins: Damage Detection and Virtual Inspection on a Reinforced Concrete Bridge, by Arnulf Hagen and Trond Michael Andersen
Asset management, condition monitoring and Digital Twins: damage detection and virtual inspection on a reinforced concrete bridge
by Arnulf Hagen, Trond Michael Andersen
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research paper explores the application of digital twins and machine learning in detecting structural defects in infrastructure, using a real-world case study on the Stava bridge in Norway. The bridge was suddenly closed due to a critical damage detected through online monitoring, highlighting the importance of proactive maintenance strategies. The authors demonstrate how combining physics-based methods with machine learning can facilitate damage detection and diagnostics. The paper also discusses lessons learned from technical and organizational perspectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Stava bridge incident in Norway showed that digital twins can help detect structural defects in real-time. The bridge was closed because sensors detected a problem before it became serious. This event demonstrated the value of technologies like digital twins for preventing accidents and reducing risks. The research uses this case study to show how combining machine learning with physics-based methods can help detect damage early on, making maintenance easier and safer. |
Keywords
» Artificial intelligence » Machine learning
