Vanessa Di Murro
Papers
2
Total Citations
35
H-Index
2
About
Vanessa Di Murro is a leading researcher in structural health monitoring and infrastructure resilience, with a specialized focus on automated damage detection for large-scale underground systems. Her work centers on developing advanced, pixel-level crack monitoring techniques using computer vision and machine learning, enabling real-time, high-precision assessment of structural degradation in critical facilities. Her most-cited paper, "Automated pixel-level crack monitoring system for large-scale underground infrastructure – A case study at CERN" (2023, 31 citations), demonstrates a groundbreaking application at the European Organization for Nuclear Research, where her system tracks crack spatial distribution in concrete tunnel linings to predict long-term structural behavior and guide maintenance. This case study, along with a similarly titled companion paper (4 citations), showcases her ability to translate theoretical methods into practical, high-impact solutions for some of the world's most demanding environments. Di Murro’s contributions are pivotal for advancing proactive infrastructure management, reducing serviceability risks, and ensuring the safety of subterranean assets—a vital achievement for both civil engineering and particle physics communities.
Research Focus
Key Achievements
Top Papers
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