This project employs novel machine learning techniques in order to enhance the sustainability of structures in terms of reduced resource consumption and CO2 emissions. Concrete is the most widely used construction material, its fabrication consumes a large number of natural resources (fine and coarse aggregates from sand and gravel), and the associated cement production contributes around 8% of CO2 emission. More than 4 billion tonnes of aggregate will be needed to meet UK’s construction demands between 2022 and 2035, according to the mineral product association (MPA). Solutions for more sustainable concrete have become imperative, among which replacing natural aggregates with recycled aggregates (RA) from construction and demolition wastes is encouraged. This can reduce prudent use of natural resources, maximise use of recycled construction waste along with potential for cost savings.
The project targets a paradigm shift in using recycled aggregates in structures. Innovative structural solutions will be developed incorporating a high volume of recycled aggregates. In the project, advanced machine learning methods and techniques will be employed and adapted for modelling constitutive behaviour of confined concrete incorporating recycled aggregates. Validation for the applicability of confined concrete material will be made against their performance in concrete-steel composite structures.
This project would suit a graduate with good knowledge in Structural Engineering and the motivation and interests in sustainability of infrastructures and the solutions for net-zero. The research will be supported by the facilities at Center of structural engineering and informatics at University of Nottingham and School of Engineering at University of Birmingham.
Applicants should have, or expect to obtain, a 1st class or 2.1 honours degree in Civil Engineering. Students with experience in Matlab/Python are encouraged to apply. We require an enthusiastic graduate with a 1st class degree in engineering, computer science, maths, or a relevant discipline, at integrated Master’s level or with a relevant MSc (in exceptional circumstances a 2:1 degree can be considered).
Due to funding restrictions this position is only available for UK candidates.
However, we welcome international candidates, funding is only available for exceptional students.
How to apply:
Applications are invited to work on a double PhD between the Universities of Nottingham and Birmingham, on application of AI for sustainable construction. Formal applications for research degree study should be made online through the http://www.nottingham.ac.uk/pgstudy/how-to-apply/how-to-apply.aspx for the University of Nottingham and https://sits.bham.ac.uk/lpages/EPS015.htm for the University of Birmingham. Please state clearly that you are applying for PHD Civil Engineering (3 years) and in the research information section that the research degree you wish to be considered for is “Machine learning-based characterisation of composite structures with recycled materials”, and choose Dr Fangying Wang and Dr Madhup Pandey as your supervisors at the University of Nottingham and Dr Jelena Ninic and Dr Konstantinos A. Skalomenos at the University of Birmingham. Alternatively, Informal contact can be sent to Dr Fangying Wang, (email@example.com ) and Dr Jelena Ninić, (firstname.lastname@example.org ) before submitting an online application. Please send a cover letter and a copy of your CV with your up-to-date relevant experience.