PhD Studentship in Condition Identification & Monitoring Data Analytics for Smart Infrastructure

University of Surrey

The safety and integrity of civil infrastructure is a critical concern in our society and a challenge for our research community. To enhance the whole life value of our infrastructure, smart infrastructure is regarded as an important pathway towards more productive delivery and effective operation of various assets.

Digital technologies, including sensing technologies, data transmission technologies, and data science, have enormous potential to transform the construction industry. Monitoring data analytics, whether physics-based or data driven, can improve maintenance efficiency and optimise asset life.

At Surrey, we have so far investigated a range of issues related to the application of such techniques in metallic bridges, pipelines, and other structures, bringing together expertise in materials and structures, as well as statistics, informatics, and decision theory. Our collaboration with various industry partners enables us to analyse real data from critical infrastructure assets and to influence the development of industry guidelines on inspection and maintenance.

This funded project on Smart Infrastructure can focus on either of the following two directions:

  1. Data-driven methods. For this direction, candidates with computer science, electrical/electronic engineering background knowledge, who are enthusiastic about data analytics, will be welcomed.
  2. Physics-based methods. For this direction, candidates with civil/structural engineering background (knowledge of structural dynamics and finite element modelling are preferred) will be welcomed.

This is an exciting opportunity for inquisitive and forward looking graduates who wish to specialise in a topic of growing importance. Skills in data analytics and a solid understanding of the potential offered by the interpretation of sensor data in infrastructure are highly desirable in many sectors, including energy, transport, and water. Our PhD graduates have an excellent track record of securing jobs both in industry and academia. In addition to the subject-specific training, the successful applicant will be able to benefit from courses and workshops offered by Surrey’s doctoral college and will have the opportunity to hone in his/her communication and teaching skills by contributing to tutorial classes and participating in seminars and conferences.

The principle supervisor, Dr Ying Wang, is an expert in structural dynamics and monitoring (https://surreyacuk-master.surrey.ac.uk/people/ying-wang). The co-supervisor, Prof. Marios Chryssanthopoulos, is a leading expert in structural risk and reliability (https://www.surrey.ac.uk/cee/people/marios_chryssanthopoulos/).

Entry Requirements

Well qualified and strongly motivated PhD candidates are invited to join an expanding research group working on SHM-based life-cycle asset management. Applicants will be selected using the following criteria:

  • Applicants should have (or expect to obtain by the start date) at least an Upper Second Bachelor’s degree, and preferably a Master’s degree, in an Engineering or Computer Science subject.
  • Applicants are expected to have background in either of the two above-mentioned directions.

Funding

For suitably qualified UK/EU applicants a full studentship is available for 3 years, including academic fees at UK/EU rate and a stipend of £14,553 per year based on the 2017/18 Engineering and Physical Sciences Research Council (‘EPSRC’) national minimum rates.

How to apply

Formal applications must be made through our programme page.
For any enquiries please contact Tina Looi (l.looi@surrey.ac.uk) for any application enquiries.

Deadline: Open until filled

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Advert information

Type / Role:

PhD

Location(s):

South East England