Architect Job: BCU PhD Scholarships for the College of Built Environment

Celebrate our outstanding research achievements in Computing, Engineering, and the Built Environment, as acknowledged in the 2021 Research Excellence Framework. We are excited to announce that the Associate Dean of Research is extending an invitation for 4x prestigious PhD scholarships within the College of Computing, scheduled to commence in May 2024.

How to Apply 

Explore the various research projects available below, and please include the Project Reference number when submitting your application. To apply, please complete the project proposal form, ensuring that you quote the project reference, and then complete the online application where you will be required to upload your proposal in place of a personal statement as a pdf document. 

You will also be required to upload two references, at least one being an academic reference, and your qualification/s of entry (Bachelor/Masters certificate/s and transcript/s). 

Project Title: An Open-source Digital Framework for Integrated Simulation via Machine Learning to Achieve Sustainable Building Safety Design in Fire

Project Lead: Dr Xu Dai Xu.Dai@bcu.ac.uk

Reference: 17 SIFBuilder

Project Description

This research project aims at bridging the gap between fire engineers and structural engineers via developing a numerical tool using C++. This research will increase wider acceptance and adoption of these more scientific approaches in fire safety engineering, specifically while interacting with structural engineering discipline.

This project will focus on the challenges of accurately estimating the demand imposed by real fire scenarios on the structure (rather than the current approach of a ‘standard fire’), and faithful simulation of the structural response using an integrated computational tool based upon modelling of real fires.

A C++ developed software, Structures in Fire Builder (SIFBuilder 2.0), with a digital framework for integrating fire and structural simulation will be developed in this project. This tool will be seamlessly bridged with machine learning and artificial intelligence (AI) packages, to enable structural fire protection optimisation for achieving sustainable building safety design under an extensive library of fire scenarios.

Anticipated Findings and Contribution to Knowledge

Overall, the PhD student will understand the fundamental limits of technologies used for the current ISAC systems and in general the multifunctional networks. There is also a need for wide understanding across different layers of the mobile network, as the industry moves quickly towards a holistic implementation of multifunctional networks for 5G and Beyond.

Publication-wise, the background for system design for multifunctional networks in cellular 5G-A and 6G systems will be surveyed and structured into a literature review paper. Technical building blocks with solution options and research challenges will be addressed and published in high-impact venues. Then, a novel PoC implementation with results will be put together into a technical and patentable article, which will show how existing 5G hardware can or cannot provide a good basis for adding sensing capabilities on top and that objects can be detected well and become separated from clutter with appropriate algorithms.

Contributions to standardisation bodies such as 3GPP may be made based on the developed solutions.

Person Specification

Candidates are encouraged to apply if having a BEng/MSc degree in one of the following subjects (or closely equivalent):

1. Structural Engineering
2. Fire Engineering
3. Software Engineering

Please also evidence your coding experience if having any.

Relevant Reading / Further Information

[1] Hall, J. R. (2014), ‘NFPA Report: The total cost of fire in the United States, Quincy’.

[2] CEN (2002), EN 1991-1-2:2002: Actions on Structures. General actions-actions on structures exposed to fire.

[3] Drysdale, D. ‘An introduction to Fire Dynamics’. 3rd ed. John Wiley & Sons; 2011.

[4] X. Dai*, S. Welch, O. Vassart, K. Cábová, L. Jiang, J. Maclean, C. Clifton, A. Usmani, (2020) ‘An extended travelling fire method framework for performance-based structural design’, Fire and Materials, 44, pp. 437-457, https://doi.org/10.1002/fam.2810

[5] X. Dai*, S. Welch, A. Usmani, (2017), ‘A critical review of “travelling fire” scenarios for performance-based structural engineering’. Fire Safety Journal, 91C, pp. 568-578, https://doi.org/10.1016/j.firesaf.2017.04.001

[6] M. Charlier*, A. Gamba, X. Dai, S. Welch, O. Vassart, J. Franssen, (2021) ‘Modelling the influence of steel structure compartment geometry on travelling fires’, Proceedings of the ICE – Structures and Buildings, 174 (9), pp. 739-748, https://doi.org/10.1680/jstbu.20.00073

[7] X. Dai*, A. Gamba, C. Liu, J. Anderson, M. Charlier, D. Rush, S. Welch, An engineering CFD model for fire spread on wood cribs for travelling fires, Advances in Engineering Software, 173 (2022) 103213, https://doi.org/10.1016/j.advengsoft.2022.103213

[8] Z. Nan, X. Dai*, H. Chen, S. Welch, A. Usmani, A numerical investigation of 3D structural behaviour for steel-composite structures under various travelling fire scenarios, Engineering Structures, 267 (2022) 114587, https://doi.org/10.1016/j.engstruct.2022.114587

[9] Q. Guan, X. Dai*, J. Ye*, S. Huang, I. Burgess, Modelling of composite fin-plate connections under fire conditions using component-based method, Engineering Structures, 264 (2022) 114451, https://doi.org/10.1016/j.engstruct.2022.114451

[10] D. Rush*, X. Dai, D. Lange, Tisova Fire Test – fire behaviours and lessons learnt, Fire Safety Journal, 121 (2021) 103261, https://doi.org/10.1016/j.firesaf.2020.103261

[11] J. Jiang, Y.L. Lu, X. Dai*, G.Q. Li, W. Chen, J.H. Ye, Disproportionate collapse of steel-framed gravity buildings under travelling fires, Engineering Structures, 245 (2021) 112799, https://doi.org/10.1016/j.engstruct.2021.112799

Project Title: Development of large language models for guiding net zero building design

Project Lead: Dr Franco Cheung Franco.Cheung@bcu.ac.uk

Reference: 18 ANODE

Project Description

This research project aims to guide the design of environmentally friendly, energy-efficient buildings, otherwise known as Net Zero Energy Buildings (NZEB). The design process for these buildings is complex and time-consuming, as it involves understanding various aspects like energy use, environmental impact, and long-term costs.

Our goal is to create an artificial intelligence (AI) program that can make this process easier. This AI will be trained to understand building designs and provide all the necessary information for NZEB design.

The first step is to develop a common language that can be understood by both the AI and the building designs. Once that is established, we’ll train the AI using these corpora and a variety of building designs along with their NZEB assessment data.

With this AI assistant, we hope to streamline the NZEB design process. This means architects and engineers can quickly access all the necessary information they need for their designs. The ultimate goal is to make the design process of environmentally friendly buildings more efficient and less time-consuming, thereby contributing to a more sustainable future.

Anticipated Findings and Contribution to Knowledge

This research extends the integration of AI in the building design field, particularly within advanced design and management tools like BIM and digital twins. It aims to deliver a transformative approach to Net Zero Emissions Buildings (NZEB) design, introducing an AI model that understands building designs and provides vital NZEB assessment information. This innovation is expected to streamline the assessment process, offering instant access to accurate data, increasing assessment reliability, and reducing manual workload.

Beyond immediate applications, this research significantly contributes to two prominent areas: The integration of generative design technology in Building Information Modelling (BIM) and the advancement of digital twins as decision-making tools.

In the context of BIM, the AI model will enhance the generative design process. Generative design uses algorithms to create optimized design options, relying heavily on accurate data. Our AI model is designed to efficiently populate BIM models with necessary data, fostering a more efficient workflow, and enhancing generative design’s contribution to NZEB goals.

For digital twins, virtual replicas of physical structures, the proposed AI model becomes instrumental. These digital counterparts require precise, real-time data. The model specifically focuses on simplifying the interpretation of building design language and generating essential NZEB assessment data. This contribution ensures that digital twins accurately capture the sustainability features of physical buildings, thereby enhancing their functionality as a robust decision-making tool.

Person Specification

We invite high-calibre graduates with a first-class BSc (Hons) or MSc in Computer Science or related fields to apply for this prestigious PhD studentship. Ideal candidates will possess a robust understanding of Natural Language Processing and Large Language Models. In your application, please articulate your relevant experience in Artificial Intelligence and Machine Learning, highlighting how your background aligns with the requirements of this position. This opportunity is tailored for individuals eager to advance in cutting-edge research domains.

Relevant Reading / Further Information

Jung, Y., Hockenmaier, J., & Golparvar-Fard, M. (2024). Transformer language model for mapping construction schedule activities to uniformat categories. Automation in Construction, 157, 105183.

Shamshiri, A., Ryu, K. R., & Park, J. Y. (2024). Text mining and natural language processing in construction. Automation in Construction, 158, 105200.

Saka, A., Taiwo, R., Saka, N., Salami, B., Ajayi, S., Akande, K., & Kazemi, H. (2023). GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation. arXiv preprint arXiv:2305.18997.

Ding, Y., Ma, J., & Luo, X. (2022). Applications of natural language processing in construction. Automation in Construction, 136, 104169.

Moon, S., Lee, G., Chi, S., & Oh, H. (2021). Automated construction specification review with named entity recognition using natural language processing. Journal of Construction Engineering and Management, 147(1), 04020147.

Project Title: Co-Creating Inclusive Active Spaces: Empowering Teen Girls through Digital Interventions

Project Lead: Dr Silvia Gullino Silvia.Gullino@bcu.ac.uk 
https://www.bcu.ac.uk/built-environment/about-us/our-staff/silvia-gullino

Reference: 19 CANVAS

Project Description

The proposed project aims to co-produce more equitable, inclusive, and active urban spaces with teenage girls through digital methods and interventions. The project acknowledges that cities are often designed by able-bodied white men for men, resulting in exclusive and dominantly masculine spaces that hinder the engagement of women and girls. The project seeks to address this issue by conducting co-production workshops with teenage girls to redesign existing and active green space. The research will involve the use of digital tools and techniques such as 3D scanning, augmented reality, and virtual reality to involve the girls in the design process and create new digital interventions to improve and co-design the spaces.

The project aligns with the CEBE vision and priorities in STEAM by using technology for the public good and addressing societal challenges:

1. Green, digital, smart, and sustainable cities, construction, environment and living/working places.
2. Emerging and advanced technologies with applications into key priority application domains such as Digital Health, Digital Built Environment, Smart Manufacturing and Digital Productivity.

The project contributes to our faculty’s equity, diversity, and inclusion goals by empowering teenage girls and addressing the underrepresentation of women in urban planning and design. The project also has internationalisation aims as the approach and outcomes can be replicated in other cities globally, contributing to more inclusive urban planning and design practices.

The project is interdisciplinary, drawing from various disciplines such as urban planning, human geography, urban design, computer science, and user experience design. The proposed team has experience in interdisciplinary research and aims to explore synergies and common ground through teenage girls’ experiences of the city.

The project’s aims and objectives include understanding teenage girls’ experiences of active spaces, co-designing inclusive and safe spaces using digital research methods, developing interactive digital interventions, and generating transferable design principles and prototypes. The project will be carried out through scoping the field, conducting co-design workshops, trialling, and evaluating digital interventions, and organising workshops with stakeholders.

The outputs and outcomes of the project include academic papers, presentations at conferences, and future grant applications. Non-academic outputs include influencing local policies and practices, creating more inclusive active spaces, and establishing partnerships for further collaboration. The project will also contribute to the development of digital society research skills for the team members and engage teenage girls in critical digital inquiry.

The project will handle data in accordance with GDPR and ethical protocols, ensuring data security, and sharing in line with guidance. Ethical considerations include obtaining consent, respecting participants’ rights and dignity, and ensuring research transparency and integrity. Ethics approval will be sought from the institutional ethics committee.

Anticipated Findings and Contribution to Knowledge

The proposed research project aims to investigate the experiences of teenage girls in urban environments and explore the potential of digital interventions in creating safe and inclusive spaces for them. The anticipated research findings from this project will represent a significant contribution to new knowledge in several key areas.

Firstly, the project will provide insights into the specific challenges and needs faced by teenage girls in urban settings. By conducting interviews, surveys, and observational studies, the research will shed light on the factors that impact their sense of safety, well-being, and participation in physical activities. This understanding will contribute to a more nuanced understanding of the barriers and opportunities for girls’ empowerment in urban environments.

Secondly, the project will explore the effectiveness of digital tools and interventions in addressing these challenges. By developing and implementing digital solutions such as mobile applications or online platforms, the research will assess their impact on the girls’ experiences and engagement. This will offer valuable insights into how technology can be harnessed to create inclusive spaces that promote girls’ agency, social connections, and physical activity.

Furthermore, the project aims to develop principles and guidelines for designing gender-responsive urban spaces. By analysing the research findings and engaging with stakeholders, the project will generate recommendations for urban planners, policymakers, and community organisations to create more equitable and inclusive cities. These recommendations will contribute to the emerging field of gender-responsive urban design and provide practical strategies for fostering safe and empowering environments for teenage girls.

The anticipated research findings from this project will contribute to new knowledge by advancing our understanding of the experiences of teenage girls in urban environments, exploring the potential of digital interventions, and providing actionable recommendations for creating more gender-responsive cities. This knowledge will have implications for urban planning, public policy, and community development, ultimately working towards the goal of creating inclusive and empowering spaces for all.

Funding

Based on the UK Research and Innovation rates for 2023 – 2024, this funding model includes a 36 month fully funded PhD Studentship, in-line with the Research Council values, which comprises a tax-free stipend paid monthly (£18,622 for 2023-2024) per year and a Full Time Home Fee Scholarship (£4,712 for 2023-2024) for up to 3 years, subject to you making satisfactory progression within your PhD research.

International students will be required to pay the difference between the International Fee Rate and Home Fee Rate. All applicants will receive the same stipend irrespective of fee status.

Application Closing Date:
23:59 on Sunday 14^th January 2024 for a May 2024 Start.

Shortlisting and Interview Dates:
Monday 15^th January 2024 – Wednesday 31^st January 2024.