REVALUE-BUILD: AI-Enabled Digital Twins for Circular Retrofit of Public Buildings

Application deadline: 04/12/2026

This 3.5 year PhD project is advertised under the Africa Futures Doctoral Scholarship Awards 2027/28 and is competitively funded. Eligible applicants will be considered through the Faculty nomination and selection process in accordance with the Africa Futures Scholarship criteria.

Successful scholarship recipients will receive an annual stipend at the UKRI minimum rate (£21,805 for 2026/27). 

Public buildings such as schools, healthcare facilities, municipal buildings and community assets represent a significant proportion of the built environment across many rapidly urbanising regions. As these assets age, refurbishment and upgrading become increasingly necessary to maintain functionality, improve performance and respond to changing social, environmental and operational requirements. However, conventional retrofit projects often lead to the premature replacement and disposal of building components that continue to possess technical, environmental and economic value.

The transition towards a circular built environment requires new approaches capable of supporting evidence-based decisions regarding the retention, adaptation and reuse of existing building resources. While digital technologies are increasingly adopted within the construction sector, their application to circular retrofit decision-making remains underdeveloped, particularly within resource-constrained settings.

REVALUE-BUILD will develop and test an AI-enabled digital twin framework that supports circular retrofit decision-making in existing public buildings. The project will investigate how building condition information, asset records, material inventories and lifecycle data can be integrated within a digital environment to identify opportunities for extending component life, improving resource efficiency and reducing avoidable waste during refurbishment projects.

The research will examine how artificial intelligence can assist in evaluating component condition, estimating remaining service potential and prioritising circular interventions. Through the development of digital representations of existing public buildings, the project will explore practical approaches for retaining, recovering, adapting and reusing building elements before replacement decisions are made.

The project will generate new knowledge on how circular retrofit strategies can be incorporated into project planning, asset management and refurbishment decision-making. Particular attention will be given to balancing environmental benefits, lifecycle costs, technical feasibility and implementation practicality. Although the research will be developed and validated through public-building case studies, its findings are expected to provide wider lessons for circular construction and sustainable asset management.

The principal outcome will be an AI-enabled circular retrofit framework capable of supporting building owners, asset managers and project professionals in maximising value retention while reducing material waste. The project aligns strongly with the global transition towards circular construction, sustainable infrastructure and responsible resource management and contributes directly to the ambitions of sustainable urban development and climate action.

Applicants should have, or expect to obtain, at least a First or Upper Second-Class Honours degree, or a Master's degree (or international equivalent), in Construction Management, Civil Engineering, Building Surveying, Quantity Surveying, Architecture, Architectural Engineering, Building Engineering, Project Management, Facilities Management, Asset Management, Sustainable Built Environment or a closely related discipline.

The project will particularly suit candidates with an interest in circular construction, sustainable built environments, digital construction, building retrofit, lifecycle assessment, asset management, artificial intelligence or digital twins. Previous research experience, professional practice or industry engagement in one or more of these areas would be advantageous but is not essential.

To apply, please contact the main supervisor, Dr Rashid Maqbool. Initial enquiries should include a current CV, academic transcripts and a brief statement outlining the applicant's academic background, research interests and motivation for pursuing the project.

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