Novel strategies for the use of machine learning in structural health monitoring. Engineering – PhD (Funded)

Structural Health Monitoring (SHM) is a highly active research area which aims at using different technologies to monitor the health of a structure. This is usually done by the collection and subsequent processing of data through networks of sensors. The applications of SHM can be numerous and the cost and 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of SHM technologies may be formulated as an intelligent maintenance strategy for structures, rather than the currently outdated, costly, and often unreliable scheduled maintenance routine.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 with a direct relevance to maintenance strategies. However, PR relies heavily on data from structures, and the acquisition of such data may pose serious challenges. For example, when dealing with expensive structures (like aircraft), it can be prohibitively expensive to acquire data under damaged conditions.

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, e.g. vibration testing, will be an advantage. Candidates with a more mathematical or computer science background, but with an interest on thepractical application of machine learning on real systems, are also encouraged to apply.

The successful applicant will be embedded in a thriving research environment, which includes the Vibration Engineering Section (VES) at the University of Exeter. VES has extensive laboratory and field testing experimental facilities, which have been significantly upgraded in the last three years through extensive investment by the University. This includes a range of actuators, sensors and data acquisition and control systems that are also suitable for use in this research project. In addition, VES has state-of-the-art analytical facilities, including FE codes, numerical analysis and control simulation.

For informal enquiries please contact: Dr Evangelos Papatheou (email: [Email Address Removed])
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 £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.

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/).

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

If you have any general enquiries about the application process please email [Email Address Removed] 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.