Concrete strength predictor
Inspired by publicly reported work at Arup
Predict concrete compressive strength from its mix and age, and show which ingredients drive it.
The brief
Arup has written about monitoring structures such as the Galecopper bridge with digital tools, turning data into decisions for engineers. Start with the material: predict concrete compressive strength from mix proportions and age, explain the ingredients that matter, and compare with what design codes would suggest. Good looks like: error in MPa against a linear baseline, explanations that match engineering knowledge, and a mix-checking tool.
Read the case first
What Arup has said in public about this kind of work, on its own pages:
Make it your own
Use mixes from your college's concrete lab or Indian standard (IS 10262) design examples as a test set, and show where the model and the code disagree.
AITeky has no link with Arup, which has not reviewed or endorsed this brief. The brief only draws on what the company has published, uses open data rather than its data, and is a learning exercise: don't present your project as Arup's work or system.
What you will build
- Explore mix proportions, age and strength
- Build a linear baseline
- Train gradient boosting and compare
- Explain the effect of each ingredient
- Build a mix-checking tool
Free, open data to build it with
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