This studentship will afford the student a chance to go deep in the areas of seismic design of steel structures, optimisation, advanced modelling, and machine learning.
- Qualification Type: PhD
- Hours: Full Time
- Title of project: Artificial neural network based prediction formula for the behaviour of perforated steel shear walls reinforced with fibre-reinforced polymer sheets.
- Placed On: 1st February 2022
- Closes: 15th May 2022, or until places have been filled.
Applications are invited for a PhD studentship in the Department of Civil Engineering. The successful candidate will have the opportunity to work on steel structures and artificial intelligence problems in the area of sustainable structural engineering.
Repair and rehabilitation of steel structures should be given more thought as they are increasingly being used in UK’s construction industry and the stock is getting old. UKRI and the Royal Academy of Engineering have heavily invested on repair and rehabilitation technologies to maintain the UK’s heritage and it is considered as one of the strong engineering expertise within the country.
In addition, as many skyscrapers are on the pipeline of the UK’s construction plan, this project is very timely.
Using fibre-reinforced polymer sheets to repair or retrofit steel structures is not only an excellent alternative to standard methods of retrofitting; FRP can be used to significantly extend the service life of steel structures too.
FRP sheets can be used for mending and rehabilitation as they have higher tensile strength, are lighter in weight, are resistant to chemical attack, can assume the shape of the parent structure, take less time to apply, and are simple to use.
Steel shear wall (SSW) systems, in particular, are going to be examined as they are often used as lateral resisting systems to retrofit medium to high-rise buildings.
These systems have special characteristics such as high capacity, distinct plastic behaviour, and high energy absorption capacity. Perforations and openings in SSW can lead to obvious severe weaknesses in the system's energy absorption, hardness, and shear capacity.
Recently a new generation of hybrid shear walls using steel/fibre-reinforced polymer (FRP) composite has been developed and demonstrates an increase in the system's strength and energy absorption capacity.
However, the modernity of the perforated shear wall system and the lack of design regulations to provide a suitable pattern for the arrangement of the holes have led to low usage. In this project, experimental tests will be validated by advanced nonlinear finite element analysis (GMNIA) and artificial neural networks (ANN) will be employed to generate accurate and reliable prediction formulations.
The proposed research will afford the student a chance to go deep in the areas of seismic design of steel structures, optimisation, advanced modelling, and machine learning. These skills are expected to be sought after highly by the industry and can also provide a robust foundation for an academic career.
The results of a successful doctoral thesis are expected to be of interest to the construction industry at large. The student will be encouraged to publish the results of their research at leading international conferences and in top-tier civil & structural engineering journals.
They will be encouraged to communicate directly with potential or actual industrial partners.
Eligibility and requirements
The candidate should have an upper second-class BEng/MEng (or equivalent, or higher) degree in civil/structural engineering. They should demonstrate aptitude for original research.
The candidate should possess a good understanding of steel structures, finite element modelling and earthquake engineering.
A candidate who demonstrates exceptional aptitude in one or more of these areas (as evidenced, for instance, through strong academic credentials or research papers in reputable, peer-reviewed journals/conferences) may be accorded preference. The successful candidate should be able to code comfortably in Python and/or C++.
A doctoral candidate is expected to meet the following pre-requisites for their PhD:
- Demonstrate a sound knowledge of their research area
- Achieve and demonstrate significant depth in at least a few chosen sub-areas relevant to their primary research area
- Demonstrate the ability to conduct independent research, including a critical assessment of their own and others’ research
Having published high-quality papers in reputable peer-reviewed conferences and journals will be an advantage for the candidate.
The studentship is for 3 years and will provide full coverage of tuition fees (Home and Overseas) and an annual tax-free stipend of £12,000.
Each student would also have the opportunity to earn around £2.2K pa on an average (max. is around £4.3K pa) through a teaching assistantship. We shall prioritise these scholarship holders while allocating the teaching assistantships.
How to apply
If you are interested in applying, you are encouraged to email initial informal enquiries to Professor Konstantinos Daniel Tsavdaridis.
Visit our Civil Engineering research degrees web page for further information on making a formal application.
When submitting your application, enter the title “Artificial neural network based prediction formula for the behaviour of perforated steel shear walls reinforced with fibre-reinforced polymer sheets” and you will automatically be considered for this studentship.
You do not need to submit a proposal as part of your application as the project has already been outlined.
The online application can be found in the ‘How to apply section’ in the web link above and should include the following supporting documents:
- Copies of Degree Certificates and Transcripts in official English translation - original will be requested before an offer is made.
- Official work e-mail addresses (not private ones) for two referees (one of which must be an academic).
- Proof of English Language proficiency (minimum average score of 6.5 IELTS, with a minimum of 6.0 in each of the four components) if English is not your first language.
The outcome of the selection process should be announced by the end of June. The successful candidate will formally start their doctorate either in July or in October 2022.
For queries regarding the application process, please email the School.
Equality, diversity and inclusion
City, University of London is committed to promoting equality, diversity and inclusion in all its activities, processes, and culture, for our whole community, including staff, students and visitors.
We welcome applications regardless of gender, sexual orientation, disability, marital status, race, nationality, ethnic origin, religion or social class. For more information on our approaches to encouraging an inclusive environment, please see our Equality, Diversity and Inclusion pages.