About the project
This project connects concrete material development and structural design to reduce carbon emissions while meeting engineering requirements. Combining machine learning, structural analysis and optimisation, the research will investigate how concrete properties, member dimensions and reinforcement influence performance and embodied carbon, helping engineers identify efficient designs for more sustainable concrete construction.
Reducing the carbon footprint of concrete construction requires decisions about both materials and structures. A concrete mix with lower emissions per cubic metre may require a larger member or more reinforcement to carry the same load. Understanding these interactions is essential to identify designs that reduce emissions while meeting engineering requirements.
This project will develop an AI-assisted approach that links concrete composition, mechanical properties and structural design. You'll build machine learning models to predict strength and stiffness, then connect these predictions to the behaviour of selected reinforced concrete members. Structural calculations will establish whether each design can carry the required loads and limit deformation during use. You'll also examine how uncertainty in material properties and emission estimates affects design choices.
The research will estimate embodied carbon from the quantities of concrete and reinforcement and their associated production emissions. An optimisation method will search for designs that lower emissions while satisfying the specified performance requirements. You'll investigate how changing material choices, member dimensions or design demands affects the available carbon savings. Predictions will be assessed against independent experimental data and structural calculations, with conventional designs providing a benchmark. The outcome will be an approach that helps engineers make informed material and design choices.
You will:
- develop practical skills in machine learning, concrete engineering and structural optimisation
- learn to assess carbon emissions alongside structural performance through realistic design problems
- build research and programming experience relevant to engineering consultancy, construction innovation and academic careers
You'll receive training in Python, machine learning, concrete mechanics and structural analysis. You'll learn how to estimate embodied carbon, search for designs that meet specified requirements, and evaluate predictions against data and engineering calculations. Training will also cover how uncertainty in material properties and emission factors affects design decisions. Professional development will support research planning, scientific writing, presentations and reproducible programming. You'll gain experience explaining the relationship between material choices, structural performance and carbon emissions to engineering audiences.
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.
