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Building Information Model Development Using Generative Adversarial Networks

Generative Adversarial Networks (GAN’s), are an approach to generative modelling using deep learning methods, such as convolutional neural networks. Generative modelling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset. GANs are an exciting and rapidly changing field, delivering on the promise of generative models in their ability to generate realistic examples across a range of problem domain and they have seen recent success in the following areas:

  • Upscaling resolutions in photos and video
  • Creating realistic movements in computer games
  • Creating 3D models from 2D pictures

This project will develop a novel GAN architecture that will act as a design assistant for the development of building information models, providing design alternatives and filling in missing information in the model as it is developed. The model will implement industry standard design principles and UK design standards to ensure that the designs are safe, efficient and constructible.

This research sits at the intersection between computer science, data management, BIM and civil engineering and will require the application of established techniques from the computer gaming and visualisation industries, for example, to a novel application in developing BIM data.

This project is a collaboration between the Department of Civil and Environmental Engineering and the AI: Social and Digital Innovation Centre. The researcher will become part of an established and high-energy research group which meets frequently and works collaboratively on a range of industry problems across numerous industries.

The candidate will be provided with mentoring toward chartered engineer (CEng) or Incorporated Engineer (IEng) status if they have an Engineering Council accredited degree.

About you

Due to the technical nature of this project, the ideal candidate will either be a computer science graduate or a civil engineering graduate with a programming background. A Masters’ degree is desired but not essential for the role.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Michael Rustell - Michael is a chartered civil engineer (CEng MICE) who holds an Engineering Doctorate (EngD) in artificial intelligence in design automation of civil infrastructure. He has spent the past 8 years in industry working in ports and marine, oil & gas and nuclear industries and was a lead data scientist for the Europe, Middle East and Africa (EMEA) region at AECOM prior to joining Brunel.  Michaels interests include: deep learning, machine learning and data science in civil engineering, natural language processing, design automation and stochastic design methods.

Tatiana Kalganova -


  • PhD Napier University
  • Research-engineer degree Belarusian State University of Informatics and Radio-electronics, Minsk, Belarus
  • MSc (distinction) Belarusian State University of Informatics and Radio-electronics, Minsk, Belarus


  • 2000-present Lecturer Brunel University London
  • 2003-2011 Business Fellow London Technology Network, LTN Link between research activities at Brunel University London and industry
  • 1997-2000 PhD student Napier University
  • 1994-1997 Research Assistant Belarusian State University of Informatics and Radio-electronics