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The application of AI and machine learning for the better performance and predictive maintenance of buildings using data from digital twins (Application Ref: ERDF20/EE/CIS/BOURIDANEAhmed)


Faculty of Engineering and Environment

Sunday, August 16, 2020 Competition Funded PhD Project (European/UK Students Only)

About the Project

The PhD project forms part of the Intensive Industrial Innovation Programme (IIIP) which is part funded by the European Regional Development Fund (ERDF). The IIIP Programme is a collaboration between Durham, Newcastle and Teesside and Northumbria Universities and aims to encourage a culture of innovation that benefits business, leading to greater export opportunities and increased graduate employment. The Programme enables small and medium-sized enterprises (SMEs) to develop new products and services. This project will be a collaboration between Northumbria University and Space Architecture (Europe) Ltd. The successful applicant will spend part (minimum 51%) of their time at Northumbria University and part at the Newcastle offices of Space Architecture (Europe) Ltd. as well as occasional visits to other locations (for example, to gather data).

The increasing sophistication of Building Management Systems and availability of smart sensors in buildings have produced a huge increase in the amount and quality of available data. However, most is currently unexploited and there is a need to make it accessible and usable for the benefit of the environment, the economy and society. The proposal aims to extend the intelligent and secure use of digital building information for the operation of safer, healthier, more environmental and more efficient buildings.

Space Group has been operating in the digital construction sector for over 15 years and is a UK leader in the field, having already developed one of the first digital twin platforms specifically for the property sector TwinView (https://www.twinview.com/). TwinView pairs virtual building models with the physical world and analyses captured operational data to optimise property outcomes.

The initial target market is UK building owners and operators with large estates, campuses or property portfolios (e.g. education, retail, commercial, healthcare, and residential) however, the product is extensible to the broader UK infrastructure (transport, energy, etc.) and beyond. The aim is to incorporate artificial intelligence (AI) and machine-learning technology to analyse and interpret unexploited data in order to identify and inform further ways to improve building operation. The proposed PhD research will require the student – a postgraduate data scientist with AI skills– to develop a prototype AI solution for intelligent use of data in the operation and predictive maintenance of buildings. The research investigation is likely to consist of three phases as follows:

• Understanding the required functionalities (through use cases) of a projected AI system and producing a logical ‘wireframe’ framework;
• Development of the system architecture and AI/ machine learning functionalities into a high-fidelity framework;
• Building a prototype system including its testing and refining with live test-case data.

This project is supervised by Professor Ahmed Bouridane. The Co-supervisor is Professor David Greenwood.

Please note eligibility requirement:

* Academic excellence of the proposed student e.g.. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
* Appropriate IELTS Score (6.5 or above), if required.

For further details of how to apply, entry requirements and the application form, see
https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. ERDF20/EE/CIS/BOURIDANEAhmed) will not be considered.

Deadline for applications: Midnight 16th August 2020

PhD Start Date: 1st October 2020

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality.


Funding Notes

The studentship is available to Home and EU students with a full stipend, paid for three years at RCUK rates (for 2020/21, this is £15,285 p.a.) and full Home/ EU Fees. The stipend will be part-funded by the ERDF grant and the University. The sponsor company will fund up to the home fee amount. Applicants from outside the UK/EU are not eligible for this award unless they are able to self-fund the difference between UK/EU domestic fees and international fees: (View Website)

References

Faraz Ahmad Khan, Fouad Khelifi, Muhammad Atif Tahir, Ahmed Bouridane “Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification Using SIFT and RootSIFT Descriptors” IEEE Transactions on Information Security and Forensics, Vol. 14, Issue 2, pp.289-303.
Tengku Mohd Afendi, Fatih Kurugollu, Danny Crookes, Ahmed Bouridane, Mohsen Farid “Frontal View Gait Recognition with Fusion of Depth Features from a Time of Flight Camera” IEEE Transactions on Information Forensics and Security, 2018, pp.1067-1082.
Faraz Ahmad Khan, Fouad Khelifi, Muhammad Atif Tahir, Ahmed Bouridane “Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification Using SIFT and RootSIFT Descriptors” IEEE Transactions on Information Security and Forensics, Vol. 14, Issue 2, pp.289-303.
Doukari, O., & Greenwood, D. (2020). Automatic generation of building information models from digitized plans. Automation in Construction, 113, 103129. https://doi.org/10.1016/j.autcon.2020.103129
Gary Storey, Richard Jiang, Shelagh Keogh, Ahmed Bouridane, Chang-Tsun Li 3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks, IEEE Access 2019, 2019, pp.121655-121664. DOI: 10.1109/ACCESS.2019.2937285
Zaher, M., Greenwood DJ and Marzouk, M. ‘Mobile Augmented Reality Applications for Construction Projects’, Construction Innovation, 18 (2), pp.152-166. https://doi.org/10.1108/CI-02-2017-0013.

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