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Underground Construction
We develop new science and digital technologies to inform underground construction operations
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Underground Digital Twins
We develop advanced modelling and machine learning techniques for underground digital twins
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New Sensors and Technology
We leverage the latest advances in optical sensing and AI to develop low cost sensors for the construction industry
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Laing O'Rourke Centre
We are based within the Laing O'Rourke Centre for Construction Engineering & Technology at University of Cambridge
Our Projects
Our Research
Live construction Monitoring
New sensors combined with machine learning and real-time feedback to inform site operatives, engineers and managers
Underground digital twins
Combining digital twins and machine learning to optimise construction operations
Advanced Experimental and Numerical Modelling
State-of-the-art techniques allow new insights into underground construction
About Us
The DCU Research Group is part of the Laing O'Rourke Centre for Construction Engineering & Technology at University of Cambridge and is led by Dr Brian Sheil. Our mission is to decarbonise and boost productivity of underground construction using digital engineering as a priority enabler. We work closely with industry partners to develop lean research-to-impact pathways. Applications we are interested include ground modelling, tunnelling, deep excavations and basements, mining, shafts and deep foundations.
- Bayesian machine learning for construction
- Underground digital twins
- Live construction monitoring
- Physics-informed neural networks for knowledge discovery
- Novel sensing technologies
- Soil-structure interaction mechanics
- Construction support fluids
Monitoring the construction of a large-diameter caisson in sand
Ronan Royston, Brian B. Sheil & Byron W. Byrne
Undrained bearing capacity of the cutting face of large-diameter caissons
Ronan Royston, Brian B. Sheil & Byron W. Byrne
Assessment of Anomaly Detection Methods Applied to Microtunneling
Brian B Sheil, Stephen K Suryasentana, Wen-Chieh Cheng
Machine Learning to Inform Tunnelling Operations: Recent Advances and Future Trends
Brian B Sheil, Stephen K Suryasentana, Michael A Mooney, Hehua Zhu
Three-Dimensional Analyses of Excavation Support System for the Stata Center Basement on the MIT Campus
Orazalin, Z., Whittle, A., and Olsen, M.
Identifying characteristics of pipejacking parameters to assess geological conditions using optimisation algorithm-based support vector machines
WC Cheng, XD Bai, BB Sheil, G Li, F Wang