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Muon scattering tomography for civil structural health monitoring

The aim of this project is to develop an MST system for passively monitoring the structural health of reinforced and steel concrete assets in nuclear environments.

Number of places

1

Funding

Home fee, Stipend

Opens

12 January 2018

Deadline

30 September 2018

Eligibility

Applicants should have (or expect) a distinction pass at Master’s level, or a first class BEng/BSc Honours degree, or equivalent, in an Engineering or Physical Sciences subject. Candidates with suitable work experience and strong capacity in experimental skills are particularly welcome to apply. Applicants must be comfortable working across disciplines and in practical lab environments. Experience of working with particle detectors, development environments (e.g. LabVIEW), and programming are all advantageous but not essential.



Project Details

The aim of this project is to develop an MST system for passively monitoring the structural health of reinforced and steel concrete assets in nuclear environments. Research will involve developing a model and a functioning portable MST detector system for passively monitoring rebar corrosion and pitting via the local changes in density from corrosion products. The project will make use of machine-learning techniques to interpret imaged data and present structural health forecasts.

This PhD provides a unique opportunity to tackle a real industry problem, while gaining multidisciplinary training in civil engineering, particule detector physics, material science and structural health monitoring. 
Muons are fundamental charged particles, produced in the atmosphere by cosmic rays. Every second, hundreds of high velocity muons shower each square meter of earth, providing a highly penetrating, naturally abundant, and safe particle source that can be used for passive imaging. One such imaging technique is Muon Scattering Tomography (MST), which uses sandwiches of detectors to measure statistical changes in the scattering angles of muons as they pass through an object. This provides a 3D map of the object’s density.

The aim of this project is to develop an MST system for passively monitoring the structural health of reinforced and steel concrete assets in nuclear environments. Research will involve developing a model and a functioning portable MST detector system for passively monitoring rebar corrosion and pitting via the local changes in density from corrosion products. The project will make use of machine-learning techniques to interpret imaged data and present structural health forecasts.

The successful applicant will be based within the Civil Automation Group (led by Dr Marcus Perry) at the University of Strathclyde, and will work closely with an expert team in muon tomography (led by Dr Jaap Velthuis) at the University of Bristol. The student will also benefit from the direct supervision of an industrial supervisor in the nuclear industry.

 

 

Further information

If you wish to discuss any details of the project informally, please contact Dr Marcus Perry.

 

Contact us

How to apply

Applicants should send a CV (with two references), a covering letter and a copy of degree transcripts to Dr Marcus Perry.


If English is not your first language, please include a copy of your English language qualifications.