Semantic Open-BIM Digital Twin Framework for Data-Driven Prefabricated Building Retrofit with Circular Energy Integration
Doctoral Training Grant Funding Information
This funding model includes a 36 month fully funded PhD Studentship, set in-line with UK Research & Innovation values. For 2025/6, this will be £20,780 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.
All applicants will receive the same stipend irrespective of fee status.
Application Closing Date:
Midday (UK Time) on Wednesday 17th September 2025 for a start date of 2nd February 2026.
How to Apply
To apply, please follow the below steps:
- Complete the BCU Online Application Form
- Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You will be required to upload your proposal in place of a personal statement on the BCU online application form.
- Upload two references to your online application form (at least one of which must be an academic reference).
- Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s).
- International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here. Please check the individual research degree course page for the required scores.
Frequently Asked Questions
To help support you to complete your application, please consult the frequently asked questions below:
Project title: Semantic Open-BIM Digital Twin Framework for Data-Driven Prefabricated Building Retrofit with Circular Energy Integration
Project Lead: Dr Ilnaz Ashayeri
Project ID: 10 - 46456649
Project description:
This PhD proposal is trying to address the urgent challenge of decarbonising the Built Environment by developing a Semantic Digital Twin Framework to support sustainable prefabricated building retrofits. Retrofitting is identified as one of the most effective strategies to reduce emissions from existing buildings, which account for a substantial portion of energy consumption and CO₂ emissions in urban areas (Gillett et al., 2025). Despite its potential, current retrofit practices are time-consuming, labour-intensive, and often lack the integration needed to scale effectively.
Prefabricated retrofitting, using modular components produced off-site, has emerged as a transformative approach. It enables faster deployment, parallel processing, and significantly reduces delays related to weather or on-site complications. Modular approaches can deliver higher quality, more energy-efficient buildings with less material waste and improved on-site safety (Mehdipoor et al., 2025). Research shows that advanced prefabrication can reduce total energy use by up to 50% compared to conventional site-built retrofits. Furthermore, certainty and precision, often lacking in retrofit delivery, can be improved by shifting key stages to controlled factory settings and leveraging automation and digital coordination (Jin et al., 2020). These benefits not only make retrofitting more sustainable but also more predictable and cost-effective.
his research proposes to leverage Open-BIM standards, particularly ifcOWL (a semantic web-based representation of the Industry Foundation Classes), to enhance interoperability and support prefabricated retrofit workflows. By semantically enriching building data, including geometry, material attributes, and lifecycle energy metrics, Open-BIM can support precise alignment between existing building conditions and retrofit component design. Enhancing ifcOWL with support for prefabrication logic and geometric precision (Pauwels et al., 2017) enables a more reliable and repeatable retrofit process.
At the heart of this approach is a semantic Digital Twin, a virtual representation of the building that evolves in real-time based on sensor data and simulation. The Digital Twin will use the Open-BIM-based IFC model as its structural foundation and be enriched with semantic rules and extensions through ifcOWL. It will integrate data from IoT sensors, such as occupancy, energy use, and thermal conditions, with BIM data to represent both current and projected states of the building. This allows the twin to support scenario testing, performance monitoring, and lifecycle planning.
Machine learning techniques will support the analytics layer of the Digital Twin. These methods will be used to process performance data, forecast energy demands, and optimise retrofit decisions. Importantly, these technologies serve as enablers, not the core of the research, to support smart, adaptable, and informed retrofit strategies.
A major innovation of this study lies in embedding circular energy principles, such as reuse, local storage, and energy sharing, into the Digital Twin. This extends the scope beyond traditional energy efficiency, aligning the framework with emerging practices in energy circularity and net-zero community design (Jradi et al., 2023). The result will be a retrofit planning tool that not only supports sustainability goals but also offers greater certainty, flexibility, and replicability in delivery.
A case study will validate the framework, assessing its usability, semantic interoperability, and impact on retrofit performance. The project is expected to produce a practical semantic Digital Twin prototype, ontology extensions for ifcOWL, and a structured decision-support method for retrofit planning. Anticipated academic outputs include journal publications on semantic BIM, Digital Twin applications, and circular retrofit frameworks. These outcomes aim to contribute to sustainable construction practices and inform industry standards for digital retrofitting approaches.
Anticipated findings and contributions to knowledge:
This research is expected to result in several significant findings and contributions. Firstly, it will deliver a novel semantic Digital Twin framework specifically tailored to the context of prefabricated building retrofits. By linking Open-BIM and ifcOWL with real-time IoT data, the project will demonstrate how semantic web technologies can enhance interoperability and coordination across stakeholders involved in sustainable retrofit delivery.
A key contribution will be the extension of ifcOWL ontologies to represent prefabrication workflows and circular energy concepts. These extensions will provide a more expressive digital language for describing retrofit components, building conditions, and lifecycle energy interactions, filling a notable gap in current semantic BIM research.
Another anticipated finding will be the identification of optimal strategies for applying Digital Twins in practice, especially in the early planning stages of retrofit projects. The research will evaluate how integrating performance data with semantic models supports decision-making, improves precision, and reduces uncertainties in project delivery.
The project will also provide new insight into how circular energy strategies, such as local energy reuse and storage, can be digitally modelled and monitored. This aligns with current UKRI and Horizon Europe research priorities, such as the UKRI’s “Retrofit Reimagined” and Horizon’s “Built4People” and “Driving Urban Transitions” partnerships, which call for scalable, digital-first retrofit frameworks and the integration of circular economy principles in construction.
Overall, the expected contribution to knowledge includes a validated technical framework, conceptual modelling tools, and practical recommendations that bridge the gap between advanced digital methods and sustainable construction practices. These findings are anticipated to inform both academic research and real-world applications, advancing the role of semantic BIM and Digital Twins in future retrofit strategies.
Person Specification:
We are seeking an enthusiastic and self-motivated candidate to undertake this PhD as part of an interdisciplinary research project developing a Semantic Digital Twin Framework to support sustainable prefabricated building retrofits. This project addresses urgent challenges in decarbonising the built environment and offers the opportunity to work at the intersection of construction management, digital innovation, and sustainability.
The ideal candidate will have a strong academic background, a keen interest in digital construction, and a desire to contribute to innovation in retrofit practices through advanced data integration and semantic technologies.
Essential requirements:
- A minimum of an upper second-class (2:1) undergraduate degree in a relevant discipline such as Construction Management, Civil Engineering, Computer Science, Data Science, Building Services Engineering, or a related field.
- A relevant Master’s degree is advantageous but not essential.
- Strong programming skills, ideally in Python, with experience in data analysis, automation, or simulation relevant to construction, energy systems, or the built environment.
- Familiarity with Building Information Modelling (BIM) concepts and workflows, particularly OpenBIM (IFC standards), and an interest in how BIM data supports retrofit or construction processes.
- Enthusiasm to engage with semantic web technologies (such as ifcOWL, RDF, OWL, SPARQL), with a willingness to learn advanced methods for data integration and interoperability.
- Excellent analytical skills, including the ability to work with quantitative data, interpret results, and translate findings into actionable insights.
- Strong written and verbal communication skills, with the ability to contribute to academic publications and work within an interdisciplinary team.
- Demonstrable passion for sustainability, decarbonisation, and digital innovation in the built environment.
Desirable attributes:
- Industry or Practical Experience: Experience in construction, particularly in areas related to modular or prefabricated systems, retrofitting, or digital construction tools, is highly valued.
- Familiarity with Research Methods: Understanding of qualitative and/or quantitative research approaches, including case study research, data analysis, or simulation.
- Interest in Applied Machine Learning or Data Science: Enthusiasm for applying data-driven methods or machine learning techniques (e.g., forecasting, optimisation) to improve energy performance or retrofit outcomes.
We strongly encourage applications from candidates with diverse backgrounds who are eager to explore innovative, interdisciplinary research that addresses real-world sustainability challenges in the built environment.
Overseas applicants:
International applicants must also provide a valid English language qualification, such as International English Language Test System (IELTS) or equivalent with an overall score of 6.5 with no band below 6.0.
Contact:
If you have any questions or need further information, please use the contact details below:
- For enquiries about the funding or project proposal, please contact: ilnaz.ashayeri@bcu.ac.uk
- For enquiries about the application process, please contact: research.admissions@bcu.ac.uk