Brian Sheil
Papers
1
Total Citations
2
H-Index
1
About
Brian Sheil is a leading researcher in the fields of geotechnical engineering and non-destructive sensing, with a particular focus on advancing underground construction and infrastructure monitoring. His major contributions center on integrating ground penetrating radar (GPR) with deep learning and autonomous robotics to enable real-time, high-fidelity imaging of buried structures. Notably, his 2025 paper on "Hybrid data generation and deep learning for GPR-based reconstruction of robotic-built underground structures" introduces a pioneering 360-degree digital reconstruction framework that uses in-pipe rotating GPR to map robotic-built tunnels and conduits. This work, already garnering 2 citations shortly after publication, addresses a critical gap in autonomous construction by providing a method to verify the geometry and integrity of underground assets without excavation. Sheil’s research has significant implications for smart infrastructure, reducing costs and risks associated with traditional surveying. His innovative blend of machine learning, sensor technology, and geotechnical principles positions him as a key figure in the future of automated underground engineering, with his work widely referenced by peers advancing digital twin and robotics applications in civil engineering.
Research Focus
Key Achievements
Top Papers
- 1