Using AI to Design Low-Carbon Concrete Formulations for Data Center Construction
Accelerated Design and Deployment of Low-Carbon Concrete for Data Centers
Concrete is the most widely used engineered material in the world with morethan 10 billion tons produced annually.Unfortunately, with that scale comes a significant burden in terms of energy, water, and release of greenhouse gasesand 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.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.Finally, we report on how these formulations were used in the construction of buildings and structures in a meta data center in a real-world deployment.
