Our

Research

FOCUS Project Research Group

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.

Geotechnical Monitoring

We develop new sensors and monitoring approaches for geotechnical engineering

Numerical and Experimental Modeling

We perform complex laboratory experiments and advanced numerical analyses

Informing Underground Construction

We research new ways of informing underground construction 

Cutting Edge Science for Industry Implementation

The FOCUS project group forms part of the geotechnical engineering research group at Oxford University. Our research interests include numerical modelling, laboratory testing at model scale, and field testing/monitoring. If you are interested in collaborating we would love to hear from you.


Current Research Projects

Design & performance of pipe-jacked tunnel drives

Student: Mr Bryn Phillips
Industry sponsor: Ward and Burke Construction Ltd

Microtunnelling is an increasingly popular means of trenchless installation for tunnels of between 600 mm and 3500 mm internal diameter and drive lengths of less than 1500m (typically). One of the greatest risks in long-drive microtunnelling is the development of excessive soil-structure penetration resistances, to the point where they exceed the total available jacking thrust. This project seeks to address this shortcoming through the development and monitoring of instrumented ‘smart’ tunnel pipes as well as bespoke reduced-scale tunnel-soil interface testing in the laboratory.

Modelling soil-structure interaction for large-diameter shafts

Student: Mr Jack Templeman
Sponsor: EPSRC

An increasingly common method of shaft construction is open-dug caisson sinking. This is a type of top-down construction in which a reinforced concrete wall is cast at the surface and progressively sunk into the ground. The sinking phase presents a number of challenges during construction including maintaining caisson verticality, controlling the rate of sinking and minimizing soil resistance through the use of lubricating fluids. This research aims to advance current understanding of soil-structure interaction for large-diameter caisson shaft construction. This will be achieved using large-deformation numerical modelling and complementary laboratory testing.

Satellite-enabled early warning system for geotechnical structures

Student: Ms Maral Bayaraa
Sponsor: Royal Commission for the Exhibition of 1851
Industry sponsor: Satellite Applications Catapult

From hundreds of km in space, satellite-Interferometric Synthetic-Aperture Radar (InSAR) analysis allow detection of ground motion to a high level of precision. However, complexities in converting the raw data to actionable information has so far limited authorities’ ability to practically monitor these structures from space. To help solve this, this research is creating a framework for geotechnical validation of the satellite measurements by developing 3D numerical models capable of simulating the underpinning mechanics of the observed soil movements. Deep learning experiments will be used to create an end-to-end scalable early warning tool, tested on historical failures and other geotechnical applications. The research will be focused on tailings dams and deep excavations.

Damage detection for critical infrastructure using computer vision & deep learning

Student: Yixiong Jing
PI: Professor Sinan Acikgoz

Ageing infrastructure places a higher demand on both the frequency and scale of inspections. These inspections are time-consuming, costly and are prone to human error. This project seeks to develop an automated, flexible, accurate and efficient process to identify existing damage in critical infrastructure. This will be achieved using a combination of state-of the art computer vision and deep learning techniques.

Behaviour of construction support fluids

Students: Mr Bryn Phillips, Ms Alicia Leong, Mr Diarmid Xu
Sponsors: Ward and Burke Construction Ltd (bentonite support fluids), EPSRC and Arup (polymer support fluids)

Bentonite- and polymer-based support fluids are widely used in the construction industry to provide temporary hydraulic support to unstable ground, for example in tunnelling works, caisson sinking and deep excavations. Whilst most civil engineering designs must account for the performance of support fluids during construction, they often do so in an ad hoc manner, relying primarily on the development of empiricism from accumulated field experience. This research will result in new design rules and testing techniques for construction support fluids, with significant benefits for the construction industry in terms of risk, material use, and time to construct. These objectives will be achieved through fieldwork, laboratory testing and theoretical analysis. 

 

Design method updating using Bayesian machine learning

Sponsor: Royal Academy of Engineering, EPSRC

While significant advances have been made in prediction modelling, uncertainty surrounding the assessment of geotechnical parameters and underground conditions (e.g. karst caverns, faults,coal veins) remains the main barrier to accurate prediction of construction behaviour. Back-analysis of soil parameters within an observational framework has been applied widely for geotechnical applications such as slope stability, pile capacity and braced excavations.   This study presents a Bayesian approach to update uncertain model parameters to inform rational strategies for optimising construction operations  using the latest monitoring data acquired during the project. The proposed framework is applied to pipe jacking and caisson construction.

Anomaly detection for microtunnelling

Sponsor: Royal Academy of Engineering, EPSRC

The proliferation of data collected by modern tunnel boring machines presents a substantial opportunity for the application of data-driven anomaly detection (AD) techniques that can adapt dynamically to site specific conditions. Based on jacking forces measured during microtunnelling, this research explores the potential for AD methods to provide more accurate and robust detection of incipient faults. A selection of the most popular AD methods proposed in the literature, comprising both clustering- and regression-based techniques, are considered.

3D ground modelling for tunnelling using probabilistic machine learning

Collaborator: Dr Stephen Suryasentana (University of Strathclyde)

An effective site investigation (SI) campaign is critical for establishing the ground parameters for tunnel design. Optimal SI planning and interpretation is therefore an important element for the future progress of cost-effective underground infrastructure. However, choosing the appropriate SI campaign (number of tests, test locations etc.) to minimise ground-related risk and uncertainty is challenging, as there is a lack of guidance in the design codes for SI planning. Therefore, the SI campaign is usually planned heuristically using ad-hoc engineering judgment. Given the large-scale nature of SI campaigns for some underground infrastructure projects, such as HS2 and the Thames Tideway Tunnel, considerable savings can be realised if a more optimal, automated and adaptive approach is adopted. This project will address these limitations through the integration of state-of-the-art probabilistic machine learning (ML) into the planning process of SI campaigns for tunnelling.

Laboratory testing of construction-induced settlements surrounding caisson shafts

Student: Mr Matthew Willoughby

There is very little guidance in the published literature relating to the soil settlements induced by the construction of large-diameter caisson shafts. This is particularly problematic if these shafts are to be located in close proximity to existing infrastructure. This research involves the development of a new apparatus to investigate the settlement induced by the construction of large-diameter caisson shafts at model scale. Construction-induced soil settlements are measured using soil imaging techniques. The laboratory measurements are being used to inform the development of new empirical design rules and numerical models

Numerical modelling of soil-structure interaction for laterally-loaded pile groups

Student: Mr Michael Watford

Pile foundations are frequently adopted to support structures founded in areas with unfavourable ground conditions and are often the only feasible solution for supporting large structures such as high-rise buildings, bridge piers and wind turbines. These foundations are typically required to resist significant lateral loads from wind, wave or current actions on the superstructure and/or the foundation itself. Accurate estimation of the role of group effects on the ‘deep-condition’ soil limiting pressure is essential for future development of more rigorous design solutions for laterally loaded pile groups. However, previous work has mainly focused on very small groups (< 5 piles). This work addresses this shortcoming using two−dimensional finite element modelling to assess the influence of group shape and size on the behaviour of laterally loaded pile groups. Additional parameters considered in the modelling include pile spacing and pile-soil interface roughness. 

Design & development of smart fibre optic force sensors

Students: Ms Irinka Lamiquiz-Pratt, Mr Jack Templeman
Sponsor: Royal Academy of Engineering

Existing sensor designs use increasingly more complex structures and strain sensing arrangements to measure specific force combinations (e.g. axial force, shear force) independent of temperature effects. This increases sensor cost as well as manufacture and calibration time. This work aims to deliver a step change in force sensor design, manufacture, and operation by fusing state-of-the art fibre optic strain sensing with artificial intelligence techniques.