About the Project
Supervisory team: Dr. Ye Li
About the project
Concrete properties depend on interactions between multiscale structures and their interfaces. This project will develop a hierarchical GNN by integrating micromechanics theory. Combining material data, numerical simulations and AI, the research will connect mechanical behaviour across scales and evaluate predictions of concrete mechanical properties.
Concrete may appear uniform, but its strength depends on how cement paste, aggregates, and their interfaces interact. Fine aggregate combines with paste to form mortar; coarse aggregate introduces another level of interaction. Thin regions around aggregate particles influence load transfer and where cracks begin to form. Predicting how these connected scales govern strength remains challenging, particularly when measurements of individual constituents are incomplete.
This project will complete and extend an emerging hierarchical graph neural network for concrete. You will develop linked models of paste, mortar and concrete to investigate how constituent properties and interfaces influence concrete mechanical properties. The central research challenge is to integrate micromechanics: the theory linking the behaviour of a composite to its constituents. You will investigate homogenisation to estimate overall stiffness from constituent properties, and model stress transfer and interface damage during loading.
These relationships will guide how the network combines information between scales. Experimental data and numerical simulations will support development and validation, including checks on intermediate material properties where measurements are available. Comparisons with conventional machine learning and mechanics models will assess whether the combined approach improves prediction and physical consistency. The model will help integrating civil engineering, material science and AI technologies.
You will:
- develop expertise in machine learning and computational mechanics through a concrete materials problem
- learn to connect material observations, physical theory and numerical models, and critically assess AI predictions
- build transferable skills for research careers in materials modelling, engineering software and AI-assisted design
You'll receive training in Python, machine learning and computational mechanics. Technical training will cover micromechanical models, numerical simulation and the transfer of material properties between scales. You'll learn how to combine experimental and simulated data, test the accuracy of model predictions, and assess whether they remain consistent with physical behaviour. Professional development will support scientific writing, research presentations and reproducible software development. You'll gain experience communicating across materials science, mechanics and AI, and explaining the strengths and limitations of the models you develop.
Entry requirements
You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:
- civil or mechanical engineering
- materials science
- computer science
- mathematics
- a related discipline
Essential skills:
- an interest in connecting AI with concrete mechanics, and a willingness to develop skills across both areas
- ability to work independently, communicate clearly and collaborate effectively
Desirable skills:
- programming experience in Python or a comparable language
- knowledge of concrete materials, solid mechanics or machine learning
- experience with machine learning, finite element modelling or experimental data analysis
- research experience and clear scientific writing
Applicants whose first language is not English must meet the University's English language requirements.
Fees and funding
We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.
Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
For more information, please visit our postgraduate research funding pages.
How to apply
Apply now
You need to:
- choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences
- select Full time or Part time
- search for programme PhD Engineering & the Environment (7175)
- add name of the supervisor in section 2 of the application
Applications should include:
- cover letter explaining your motivation and suitability for the project
- research proposal (1 or 2 pages)
- your CV (resumé)
- 2 academic references
- degree transcripts and certificates to date
- English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
For questions about applying, please [contact details available in the full listing].
Project leader
For an initial conversation, [contact details available in the full listing].
The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees' well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
