Structural health monitoring (‘SHM’) of civil infrastructure
The University of Surrey are seeking a motivated and enthusiastic doctoral student to work in a fast-paced, progress-oriented environment for a funded PhD studentship.
Funding is available for UK or EU nationals only and covers full tuition fees (home rate) and a stipend at the rate specified by the Research Council (rate for 2017/18 is £14,553 p.a. tax-free).
Funding sourceUniversity of Surrey
The safety and integrity of civil infrastructure is a critical concern in our society and a challenge for our research community. To address such issues, next-generation materials and structures have been envisioned as being engineered with smart features and abilities to monitor their own condition through a comprehensive sensor network in either passive or active ways.
These sensors should be able to evaluate structural integrity indicators and provide maintenance/management recommendations. In this respect, the huge quantities of Structural Health Monitoring (SHM) data that can be acquired offer not only opportunities to help engineers improve the safety and maintainability of critical structures, but also introduce new challenges which require further advances in fundamental research and applied technologies.
At Surrey, we have so far investigated a range of issues related to the application of SHM techniques in metallic bridges, pipelines and other structures, bringing together expertise in materials and structures, as well as statistics, informatics and decision theory. Our collaborations with various industry partners enables us to analyse the real data from the critical infrastructure assets.
Well qualified and strongly motivated PhD candidates are invited to join an expanding research group working on SHM-based life-cycle asset management.
- Applicants should have (or expect to obtain by the start date) at least a 2:1 bachelors degree, and preferably a masters degree, in an engineering subject
- Applicants are expected to have excellent analytical skills and a solid background in structural mechanics/dynamics and advanced numerical modelling of structures (e.g., finite element modelling)
- Applicants with a deep understanding of statistical inference and/or machine learning techniques will be preferred
- Relevant professional experience is welcomed.
How to apply
Applications can be made through our Civil and Environmental Engineering PhD course page. In your application you must mention this studentship in order to be considered.
This studentship is now closed for applications.