Condition identification and Monitoring data analytics for Smart infrastructure

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/).

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