• Underground Construction

    We develop new science and technology to inform underground construction operations

    Join Research
  • New Sensors and Technology

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

  • Advanced Modelling

    Laboratory modelling and numerical simulations underpin design methods tailored for industry 

  • Geotechnical Engineering

    We are based within the geotechnical research group at Oxford University


Geotechnical Monitoring

New sensors & technology including machine learning and real-time feedback

Experimental and Numerical Modelling

Laboratory (reduded-scale) modeling and advanced numerical analyses

Field Deployment and Validation

Data-driven decision-making and new design methods







About Us

The FOCUS (Intelligent Fibre Optic Monitoring to Inform the Construction of Underground Services) project is funded through the Royal Academy of Engineering and the Engineering and Physical Sciences Research Council. Alongside a range of industry partners, we develop intelligent, automated methods for instrumenting, measuring and monitoring soil-structure interaction during underground construction processes including, but not limited to, tunnelling, deep excavations and large-diameter shafts.

A particular focus of our research has been in the area of soil-structure interaction (SSI) covering development of normal and frictional contact stresses exerted by soil onto structures, soil strength mobilization displacements, pore water pressures and their time dependence. Our research has included the use of numerical modelling, laboratory testing at model scale, and field testing/monitoring.

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


Dr. Zhandos Orazalin

Postdoctoral Researcher

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

Visiting Researcher

Jingkang Shi

Visiting Researcher