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Biography:
Arjan Dexters is a BRE trust funded PhD student for the BRE Centre for fire safety at the University of Edinburgh, where he conducts research in Machine Learning techniques to predict the fire behaviour of materials in large-scale tests. After high school he worked for 4 years. Two of which as a dive instructor, where he learned the essence of responsibility and communication. In the following period, his hands-on experience at a construction company taught him that engineering knowledge is paramount in order to fully understand innovative construction applications. For this reason he enrolled at the University of Ghent, where he obtained the BSc in Engineering: Architecture (2016, Cum Laude). After which, he graduated from the International MSc in Fire Safety Engineering, jointly hosted by the Universities of Ghent, Edinburgh and Lund (2018, Cum Laude).
Academic Qualifications:
International MSc, 2018
Fire Safety Engineering
Distinction: Cum Laude
University of Ghent, Edinburgh and Lund
BSc, 2016
Engineering: Architecture
Distinction: Cum Laude
University of Ghent
A-level, 2008
Economics and mathematics
Distinction: Cum Laude
Belgium High School
Professional Qualifications and Memberships:
CAD Technician/Draughtsperson
Ramen Dexters BVBA | Belgium | 2010 – 2012
- Created and maintained detailed CAD drawings concerning the connection between aluminium joinery and structural work for a variety of projects
- Ensured all drawings were in compliance with existing regulations
- Communicated the design with both the construction workers and the various shareholders
Scuba Diving Instructor
Time To Dive | Belgium | 2008 – 2010
- Trained students in the safe and correct use of Scuba gear for both recreational and technical diving
- Provided diving first aid courses, e.g., basic life support and CPR
- Planned and organized dive trips to a variety of locations around the world
Research Interests:
PhD, funded by the BRE trust
University of Edinburgh | United Kingdom | 2018 – present
- The research project aims to provide a more complete characterisation of the fire performance of construction products or configurations by providing a machine-learning algorithm for assisting with defining performance in a broader range of current and innovative hazard scenarios.
Master thesis: “Cost-Benefit Analysis of Fire Spread Scenarios in Large Compartmentalized Warehouses”
University of Edinburgh | United Kingdom | 2018
A computer model was made to calculate the different scenarios of fire spread in large compartmentalized warehouses. In addition, a probabilistic risk assessment was conducted to predict the probability of flashover with and without safety measures. The final result was a hands-on tool for the private investor, which can aid in choosing the most beneficial safety measure
