  Concrete is the most widely used engineered material in the world with more
than 10 billion tons produced annually. Unfortunately, with that scale comes a
significant burden in terms of energy, water, and release of greenhouse gases
and other pollutants; indeed 8% of worldwide carbon emissions are attributed to
the production of cement, a key ingredient in concrete. As such, there is
interest in creating concrete formulas that minimize this environmental burden,
while satisfying engineering performance requirements including compressive
strength. Specifically for computing, concrete is a major ingredient in the
construction of data centers.
  In this work, we use conditional variational autoencoders (CVAEs), a type of
semi-supervised generative artificial intelligence (AI) model, to discover
concrete formulas with desired properties. Our model is trained just using a
small open dataset from the UCI Machine Learning Repository joined with
environmental impact data from standard lifecycle analysis. Computational
predictions demonstrate CVAEs can design concrete formulas with much lower
carbon requirements than existing formulations while meeting design
requirements. Next we report laboratory-based compressive strength experiments
for five AI-generated formulations, which demonstrate that the formulations
exceed design requirements. The resulting formulations were then used by Ozinga
Ready Mix -- a concrete supplier -- to generate field-ready concrete
formulations, based on local conditions and their expertise in concrete design.
Finally, we report on how these formulations were used in the construction of
buildings and structures in a Meta data center in DeKalb, IL, USA. Results from
field experiments as part of this real-world deployment corroborate the
efficacy of AI-generated low-carbon concrete mixes.
