Ageing & Remaining-Life Modelling
Classifying how engineered materials degrade, then estimating remaining service life from physical evidence rather than age alone — validated on buried pipe.
01 — The path
How far a training can travel
I trained as an architect. I now build models that predict when buried infrastructure will fail. The distance between those two is the point — and an interdisciplinary education is what made it crossable.
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2007–2012
Architectural Engineering
Bachelor
Where I learned to design physical things that have to work — materials, loads, and the distance between a drawing and what actually gets built.
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2014–2016
Architecture
Master, abroad
I moved to continue in the same discipline and go deeper into it.
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2016–2019
REAP
Master, interdisciplinary
Resource Efficiency in Architecture and Planning — interdisciplinary by design, and the turning point. This is where I understood that what I had learned could be carried much further than the field it came from.
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2019 – now
Where that took me
Far transfer, applied
Infrastructure engineering. System reliability. Laboratory methods never used this way before. Machine learning. Different fields — the same training, carried across.
None of it was only study. There was work beside every stage — drafting, then three years in an architecture practice, then an internship abroad during the master's, then building the software that runs laboratory equipment.
Far transfer is the thread. Each step carried the one before it into a field it was not built for — which is why measuring pipe density with X-ray microscopy, or turning laser spectroscopy on polyurethane foam, was the obvious move rather than a leap.
