Novel strategies for the use of machine learning in structural health monitoring Ref: 3132

About the award

Structural Health Monitoring (SHM) is a highly active research area which aims at using different technologies to monitor the state (or health) of a structure - usually by the collection and subsequent processing of data through networks of sensors. The applications of SHM can be numerous and the cost (and even safety) benefits significant for various industries including aerospace (civil and military), civil infrastructure, and renewable energies (e.g. wind turbines) among others. Ultimately, the vision for the use of SHM technologies may be formulated as an intelligent maintenance strategy for structures (or systems of structures), rather than the currently outdated, costly, and often unreliable scheduled maintenance routine.


Among the various approaches to SHM, Pattern Recognition (PR) is becoming very popular. It can be combined with probabilistic models and provide reliable and robust solutions. However, PR relies heavily on data from structures and the acquisition of such data may pose serious challenges, especially when reliable data from expensive structures (like aircraft) under damaged conditions are required – as this can be prohibitively expensive to acquire. This project will focus primarily on vibration-based SHM with a PR approach, which will require the use of machine learning tools, and will aim to develop strategies to deal with the lack of reliable training data for the machine learning algorithms. A combination of suitable experimental strategies and numerical modelling, such as Finite Element Analysis (FEA) is expected to be a key to the project’s success.


The ideal candidate should have a suitable engineering degree (e.g. mechanical/civil engineering) or equivalent, and strong numerical modelling (FEA) skills. Experience or familiarity with machine learning approaches or with experimental testing (like vibration testing) will be an advantage.


For informal enquiries please contact: Dr Evangelos Papatheou (email: e.papatheou@exeter.ac.uk)


This award provides annual funding to cover UK/EU tuition fees and a tax-free stipend.  For students who pay UK/EU tuition fees the award will cover the tuition fees in full, plus at least £14,777 per year tax-free stipend.  Students who pay international tuition fees are eligible to apply, but should note that the award will only provide payment for part of the international tuition fee and no stipend.


The studentship will be awarded on the basis of merit for 3.5 years of full-time study to commence in September 2018.

 

 

Summary

Application deadline:15th May 2018
Number of awards:1
Value:14,777
Duration of award:per year
Contact: Postgraduate Research Support Officeemps-pgr-ad@exeter.ac.uk

How to apply

 

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”. 


• CV
• Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English


The closing date for applications is midnight on 1/05/ 2018.  Interviews will be held on the University of Exeter Streatham Campus the week commencing 15/05/2018.


If you have any general enquiries about the application process please email emps-pgr-ad@exeter.ac.uk or phone +44 (0)1392 725150.  Project-specific queries should be directed to the main supervisor.

Please quote reference 3132 on your application and in any correspondence about this project.

 

Entry Requirements

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science and technology. 


If English is not your first language you will need to have achieved at least 6.5 in IELTS and no less than 6.0 in any section by the start of the project.  Alternative tests may be acceptable (see http://www.exeter.ac.uk/postgraduate/apply/english/).

Supervisors

Dr Evangelos Papatheou, University of Exeter