Digital Twins for Structural Health Monitoring. EMPS College Home fees Studentship, PhD in Engineering.

Location:

Department of Engineering, Streatham Campus, Devon, University of Exeter

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences is inviting applications for a fully-funded PhD studentship to commence in January 2022 or as soon as possible thereafter. The studentship will cover Home tuition fees plus an annual tax-free stipend of at least £15,609 for 3.5 years full-time, or pro rata for part-time study. 

This College studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate.

Project Description:

Structural Health Monitoring for civil infrastructures is a critical technology in an urgent need of development to underpin the prosperity of the society. Civil infrastructures, commonly aged over their design limit, have been often loaded over their design loading to carry the increased demand of the modern society. To maintain such ageing infrastructures safely and cost-effectively, the society requires technologies for 1) quantitative assessment on the structural health and 2) preventive restrengthening planning. However, they are still in their infancy in the research community.

A Digital Twin is a computer model of a civil infrastructure, which helps structural engineers and owners of the structure figure out what exact condition it has and which preventive actions are required in the near future to achieve cost-effective maintenance, together with the guaranteed structural safety. After a decade of the conceptual discussion in the research community, Digital Twin has now a enabling technology called the Deep Learning, which is highly likely to change daily life of us without exception for structural health monitoring.

In this research, the PhD student investigates how to develop Deep Learning to create Digital Twins of civil infrastructures. In collaboration with Alan Turing Institute (https://www.turing.ac.uk/) consisted of data-scientists aiming for data-centric engineering, the PhD student develops the promising concept of Deep Learning based Digital Twins and applies it to the MX3D bridge, the world's first 3D printed Bridge in Netherland (https://mx3d.com/industries/infrastructure/mx3d-bridge/). For a larger scale, this concept will be applied to long-span bridges, candidates are Tamar Bridge and Clifton Suspension Bridge.

Entry Requirements:

This studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate.

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 or technology.

If English is not your first language you will need to have achieved at least 6.0 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/).