You’re a geotechnical engineer.
You’ve heard about AI and machine learning. Maybe you’re curious. Maybe you’re skeptical.
Either way, you’re wondering: Can this actually help me do my job better?
That’s what this newsletter is about.
What STAIGES Means
Smart Tech and AI for Geotechnical Engineers.
It’s not just a newsletter, it’s a community of geotechnical engineers figuring out how to use machine learning in practice.
Not someday. Not in theory. Now, on real projects.
What You’ll Get Every Week
Practical ML applications in geotechnical engineering. Not hype. Not research papers you can’t implement. Just real examples you can understand and use, like:
- Using CPT data to predict soil conditions across your site.
- Building 3D subsurface models from borehole data.
- Making complex analyses faster with surrogate modeling.
- Handling incomplete data better with probabilistic methods.
- Reliability-based design that actually fits project timelines.
If you’re sitting on piles of site data and wondering what more you could do with it, this is for you.
Who Writes This
I’m Laith Sadik, a licensed PE with a PhD in geotechnical engineering. I work in consulting, publish research on ML applications in geotechnical engineering, and serve as a reviewer for tens of journals.
I’ve spent the last decade in this field doing research, consulting, and fieldwork. I chair conference sessions on AI/ML in geotechnical engineering and try to stay on the cutting edge of what’s possible.
But here’s what matters more:
I believe the best insights come from collaboration.
One person doesn’t have all the answers. The field is evolving too fast, and the problems are too diverse.
STAIGES is where we share what’s working, what’s not, and what we’re learning. You’ll get my insights every week, but I want to hear yours too. What are you trying? What’s failing? What’s succeeding?
My Research Focus
I work on three areas where I think ML is changing geotechnical practice:
- General ML models for geotechnical applications (which algorithms for which problems)
- Reliability-based design using machine learning (making probabilistic analysis practical)
- Enhanced subsurface investigation through AI (better predictions from sparse data)
You’ll see these themes show up regularly in the newsletter, along with whatever else the community is discovering.
Join the tribe
Subscribe if you want to see what’s possible. Share what you’re learning. Let’s figure this out together.
If you have any comments or things that could help our readers, please let me know using the email below:
STAIGES: Insights 