AI-driven development and design of meta-composites for constructions.

About the Project

Composite materials (cement-based or polymer-based) have been extensively used for decades in the construction, repair, and strengthening of infrastructure and the built environment. Despite their widespread application, the design of these materials has largely relied on trial-and-error approaches, which are time-consuming, susceptible to human error, and difficult to generalize to emerging composite systems with tailored functionalities.

This PhD project aims to overcome these limitations by developing novel artificial intelligence and machine learning frameworks for the design of next-generation meta-composites. Inspired by natural systems, the research will focus on creating efficient, multifunctional, and multi-objective composite materials with enhanced mechanical performance, durability, and sustainability. AI-driven models, tightly coupled with physics-based laws, will be developed to establish robust structure–property–performance relationships, enabling optimal material performance with minimal material usage.

Depending on the candidate’s interests, the project may focus on cement-based or polymer-based composites. Digital manufacturing, experimental testing, and computational modelling will be employed to validate the proposed models and to evaluate the performance of the optimized materials. The project offers a highly interdisciplinary research environment at the intersection of artificial intelligence, materials science, and structural engineering, with strong potential for real-world impact in sustainable construction.

We are seeking an enthusiastic and highly motivated home/UK student with good interpersonal skills and a keen interest in research. You must have, or expect to achieve, at least a 2:1 honors degree or a distinction or high merit at MSc level (or international equivalent) in Civil Engineering, mathematics or a related subject. Preference will be given to candidates with educational and/or research experience in engineering. Experience in experimental materials characterisation is essential. The candidate will be expected to have good interpersonal skills. For further details and discussion about the position, please contact Dr Bahman Ghiassi (b.ghiassi@bham.ac.uk).

Funding Notes

This project is only for self-funded students.