Digital Stone: Robotic Construction of a Masonry Arch Bridge
Stone bridges and buildings were widely built up until the 1920s. They last for hundreds - and in many cases thousands of years, for example those built by the Romans. With 80% lower carbon emission compared to concrete or steel, stone is also the most sustainable construction material for long-term projects.
Traditional stone construction technology is suitable for individual bridges but needs to be updated for wider-scale applications. By combining traditional stonemasonry with current technology, the Digital Stone Bridge Construction sector will offer a feasible and sustainable alternative for the 21st century.
The aim of this PhD project is to develop a novel AI system for autonomous stone arch bridge construction. The candidate will work with robotic arms within the AI Centre laboratory, including the high-precision Franka Emika cobot (https://www.franka.de/) which has 7 degrees of freedom and is capable of complex manoeuvres and will focus on developing the control algorithms that will enable the robot arm to automatically construct a scale-model for a 1-2m span masonry arch bridge.
The specific technologies that will be developed are:
1) Computer vision system for identifying stone units and determining optimal gripping locations for safe pick up
2) Robotic control system to ensure safe and smooth movement of stone units from stockpile to end location, as well as safe and smooth placement of the units
3) A bi-directional digital twin of the bridge, to be used for planning construction sequences and updating the construction process in real-time.
The research sits at the intersection between robotics, structural engineering, computer science and is a collaboration between the Department of Civil and Environmental Engineering and the Centre for Artificial Intelligence (AI). The researcher will become part of an established and dynamic research group which meets frequently and works collaboratively on a range of industry problems across numerous industries.
Due to the technical nature of this project, the ideal candidate will either be a robotics, 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:
- 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.
- 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.
- Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.
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: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. 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 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.
- 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