The University of Manchester — Department of Civil Engineering and Management
Supervisors: Dr Rashid Maqbool, Dr Akilu Yunusa-Kaltungo, Dr Mohamed Abadi
Applications accepted all year round
Competition Funded PhD Project (Students Worldwide)
About the Project
Buildings are commonly treated as final products, yet each building is also a temporary repository of steel, concrete, timber, façade systems, mechanical equipment and other potentially valuable resources. When buildings are refurbished or demolished, limited information about the condition, recoverability and future suitability of these materials means that substantial value can be lost.
This PhD will investigate how artificial intelligence, Building Information Modelling, digital twins and dynamic material passports can transform buildings into intelligent material banks. The project will develop a cognitive digital twin capable of understanding what materials exist within an asset, monitoring how their condition and value change, predicting when they may become available, and recommending where and how they could be recovered and reused.
The research will go beyond conventional static material inventories by creating a continuously updateable digital representation of material identity, condition, embodied carbon, residual value, location, disassembly requirements and potential future applications. Artificial-intelligence models will be developed to forecast material recovery, assess reuse suitability and match recovered components with demand from future projects. A decision-support system will then compare alternative circular strategies using environmental, technical, economic and logistical criteria.
The successful candidate will employ a combination of systematic review, stakeholder interviews, BIM and digital-twin modelling, machine learning, lifecycle assessment, material-flow analysis, optimisation and construction case studies. Depending on data availability, validation may involve a new-build project, an existing asset undergoing retrofit, or a building approaching deconstruction.
The project offers an opportunity to work at the intersection of construction management, civil engineering, artificial intelligence, data science and circular economy. The student will develop advanced capabilities in Python, BIM, digital twins, material passports, lifecycle assessment, data modelling and multi-criteria decision analysis. They will also receive training in research design, publication, stakeholder engagement and the responsible use of construction data and artificial intelligence.
Expected outcomes include a cognitive digital-twin framework, dynamic material-passport architecture, AI-enabled material-recovery and reuse models, a circular decision-support prototype and practical implementation guidance for construction organisations and policymakers.
The research responds directly to the need for autonomous, intelligent and adaptive construction ecosystems, where digital intelligence can optimise material, energy and information flows across the asset lifecycle. It will provide the successful candidate with an opportunity to contribute to high-impact research addressing resource scarcity, construction waste, embodied carbon and the transition towards a net-zero built environment.
Before you apply: We strongly recommend that you contact the supervisors for this project before you apply.
How to apply:
To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships. If you don't already have an applicant account, please follow the instructions here.
When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications. Please note late or incomplete applications will not be considered.
Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status.
We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder.
The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD (or international equivalent) OR any upper-second class (2:1) honours degree and a Master's degree at merit in a discipline directly relevant to the PhD (or international equivalent). Previous research experience in BIM platforms or information modelling; Python, R or another analytical programming language; machine learning or data science; digital twins, IoT or sensor data; lifecycle assessment or embodied-carbon analysis; GIS, databases or ontology development; optimisation or decision-support methods; construction material management, retrofit or deconstruction; qualitative interviews or industry case-study research is desirable.
This project will remain open until filled.
If your application is submitted by 1st November 2026, you can expect a decision by 18th December 2026.
If your application is submitted by 15th January 2027, you can expect a decision by 30th March 2027.
Self or externally funded students can also be considered for this project.
FSE_SoE
Funding Notes
Funding for this project covers tuition fees, UKRI minimum annual stipend (currently £21,805/annum) and up to a £2.5k/annum research training support grant for the full duration of the 3.5-year programme.