Accelerated Design and Deployment of Low-Carbon Concrete for Data Centers

Xiou Ge, Richard T. Goodwin, Haizi Yu, Pablo Romero, Omar Abdelrahman, Amruta Sudhalkar, Julius Kusuma, Ryan Cialdella, Nishant Garg, Lav R. Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationProceedings of the 4th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, COMPASS 2022
PublisherAssociation for Computing Machinery
Number of pages13
ISBN (Electronic)9781450393478
StatePublished - Jun 29 2022
Event4th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, COMPASS 2022 - Virtual, Online, United States
Duration: Jun 29 2022Jul 1 2022

Publication series

NameACM International Conference Proceeding Series
VolumePar F180472


Conference4th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, COMPASS 2022
Country/TerritoryUnited States
CityVirtual, Online


  • artificial intelligence
  • concrete
  • sustainable building materials
  • variational autoencoders

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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