Artificial intelligence to develop tailored construction materials (ENG1361)

The main aim of this project is to use artificial intelligence algorithms for optimization of the composition of construction materials, to maximize their durability and resistance mechanical and environmental loads.

We aim to revolutionize the design of construction. Most of the construction materials used worldwide are concrete and asphalt; the problem is that their design, in many cases, is still made by the trial-and-error method, which is a lengthy process and is prone to human errors. To fully automatize the design of materials with extreme durability under realistic service life conditions, the successful candidate will combine advanced machine learning techniques and Multiphysics computational tools in a virtual reality environment.

Innovative aspects of this thesis will be (i) the computational inpainting of virtual asphalt or concrete; (ii) the application of an innovative discrete Multiphysics modelling method; (iii) the use of optimization and unsupervised machine learning algorithms to define the asphalt or concrete composition that maximize the durability.

The outcome of the project will be a computational tool that can be used by the construction industry to design asphalt or concrete with extreme durability and the lowest cost, under given service conditions. In order to ensure the applicability of the software for the industrial sector, the candidate will interact with several construction companies and industrial laboratories during the project.
Entry requirements:
We are seeking an enthusiastic and highly motivated person with good interpersonal skills and a keen interest in research. You must have at least a 2:1 honors degree or a distinction or high merit at MSc level (or international equivalent) in physics, materials science, mathematics or engineering.

This studentship is open until filled. Early application is strongly encouraged.

To apply for this position, send the CV directly to Dr Bahman Ghiassi ([Email Address Removed]) or Dr Alvaro Garcia ([Email Address Removed]). Informal enquiries about the vacancy can also be asked to the same contact persons.

Please apply here https://www.nottingham.ac.uk/pgstudy/how-to-apply/apply-online.aspx

When applying for this studentship, please include the reference number (beginning ENG and supervisors name) within the personal statement section of the application. This will help in ensuring your application is sent directly to the academic advertising the studentship.