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Dr Kay Rogage

Assistant Professor

Department: Computer and Information Sciences

I am a Research Fellow in Digital Living. An active member of Northumbria’s BIM Academy Research Team, my work spans across the disciplines of computer science and the built environment with a focus on enabling the interoperability of building models and tools to overcome the technical barriers around data exchange in the built environment. My work also focuses on the automatic augmentation of Building Information Model (BIM) data using Machine Learning techniques.

My previous roles have included Research Associate at Newcastle University and for Royal Institute of British Architects (RIBA) Enterprises developing a number of software products for the National Building Specification (NBS). I have also worked in a number of new digital media organisations such as TH_NK developing solutions for a range of digital formats. As a lecturer at Northumbria’s Computer Information Sciences Department, I apply my research and knowledge to both undergraduate and postgraduate taught programmes.

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  • Please visit the Pure Research Information Portal for further information
  • Decarbonising Construction Equipment: Management Practices and Strategies for Net Zero in UK Infrastructure Projects, Saif, W., Rogage, K., Martinez, P., Kassem, M. 15 Feb 2025, In: Building and Environment
  • 3D object recognition using deep learning for automatically generating semantic BIM data, Rogage, K., Doukari, O. 1 Jun 2024, In: Automation in Construction
  • Can Machine Learning Automate Carbon Classification of Materials Within a BIM?, Abdulkadir, A., Rogage, K. 17 Jul 2024, Proceedings of the 2024 European Conference on Computing in Construction, Newcastle upon Tyne, UK, European Council on Computing in Construction (EC3)
  • Inductive Theorization of Construction Digital Twins: Deriving A Taxonomy of Applications and Enabling Technologies, Saif, W., RazaviAlavi, S., Rogage, K., Xie, X., Kassem, M. 14 Jul 2024, Proceedings of the 2024 European Conference on Computing in Construction, European Council on Computing in Construction (EC3)
  • Malware and botnet prevention in smart building IoT devices using blockchain-enabled federated learning, Taylor, C., Issac, B., Aslam, N., Rogage, K., Kelly, G. 24 Jun 2024, International Conference on AI and the Digital Economy (CADE 2024), Stevenage, IET
  • Reporting on the Development of a Web-Based Prototype Dashboard for Construction Design Managers, Achieved through Design Science Research Methodology (DSRM), Gledson, B., Rogage, K., Thompson, A., Ponton, H. 25 Jan 2024, In: Buildings
  • Addressing Design Management Challenges Through the Development of a Web-Based Platform, Gledson, B., Rogage, K., Luk, M., Tallet, E., Thompson, A., Landy, M., Wiggett, D. 5 Sep 2022, Proceedings of the 38th Annual ARCOM Conference, Association of Researchers in Construction Management (ARCOM)
  • Automating excavator productivity measurement using deep learning, Mahamedi, E., Rogage, K., Doukari, O., Kassem, M. 22 Apr 2022, In: Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction
  • Beyond digital shadows: A Digital Twin for monitoring earthwork operation in large infrastructure projects, Rogage, K., Mahamedi, E., Brilakis, I., Kassem, M. 28 Dec 2022, In: AI in Civil Engineering
  • Developing an Improved Process Model for Forensic Analysis of Construction Project Delays, Boyacioglu, S., Greenwood, D., Rogage, K., Parry, A., Gledson, B., Hinds, M. 5 Sep 2022, Proceedings of the 38th Annual ARCOM Conference, Association of Researchers in Construction Management (ARCOM)

Jess Tindall The application of advanced optimisation techniques to drive improvements in the automation and efficiency of deep learning building energy prediction modelling. Start Date: 13/02/2024

  • Computing Science PhD December 27 2013
  • Media Studies MA September 01 2007
  • Computing Science BSc June 20 2001
  • Fellow (FHEA) Higher Education Academy (HEA) 2016


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