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Smarter Geotech Starts With Digital Twins
Over the past year, I’ve come across the term “digital twin” quite a bit, a term I really like because it sounds pretty cool! So I started gathering information to see how it could be applied in geotechnical engineering. This week’s article is all about that. I’ve never applied it myself, but I hope this…
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More Data, Better Model? Not Always!
At the GeoCongress 2023 conference in LA, I had an interesting conversation with a geotechnical engineer about a Bayesian correlation model we developed. The model correlated cone penetration tip resistance with standard penetration test (SPT) blow counts in sandy soils, using 220 high-quality data points from previous research. His reaction? “Wait, just 220 points? And…
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Simplicity vs. Complexity in ML Models: Are We Overcomplicating Things in Geotechnical Engineering?
A while back, we wrote a research paper on this very topic (I’ll drop the link below), and the question still stands: Do we really need complex models all the time? Over the past two decades, AI and ML have made their way into geotechnical engineering, and it’s been exciting to see. Researchers have developed…
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Your Geotechnical Machine Learning Model is Probably Unusable – Here’s Why
Over the past three years, I’ve had a great opportunity to review tens of research papers applying machine learning to geotechnical problems: slope stability, soil classification, predictive modeling, you name it. And while I genuinely appreciate the effort researchers put into these studies, I’ve noticed a pattern: Many of them are just chasing the AI/ML…
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Will AI Replace Geotechnical Engineers?
It was August 2021 when I first joined the PhD program at UC. Apart from face masks, all grad students were talking about Python, programming, and machine learning—and I had no idea what they meant. Coming from traditional geotechnical field work with a heavily experimental research background, I thought civil engineering was purely classical science.…