• Underground Construction

    We develop new science and digital technologies to inform underground construction operations

    Join Research
  • Underground Digital Twins

    We develop advanced modelling and machine learning techniques for underground digital twins

  • New Sensors and Technology

    We leverage the latest advances in optical sensing and AI to develop low cost sensors for the construction industry

  • 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.

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

Dr. Brian Sheil

Group PI

Dr. Geyang Song

Postdoctoral Researcher

Jack Templeman

DPhil Researcher

Bryn Phillips

DPhil Researcher

Maral Bayaraa

DPhil Researcher

Yixiong Jing

DPhil Researcher

Alex Swallow

DPhil Researcher

Daniel McNamara

DPhil Researcher

Kevin O’Dwyer

DPhil Researcher

Jiaxu Zuo

DPhil Researcher

Yuling Max Chen

DPhil Researcher

Pete Hensman

MSc Researcher

Pin Zhang

Newton International Fellow (Royal Society)

Asad Wadood

PhD Researcher

Wei Lin

Visiting scholar