TY - GEN
T1 - Accelerated Design and Deployment of Low-Carbon Concrete for Data Centers
AU - Ge, Xiou
AU - Goodwin, Richard T.
AU - Yu, Haizi
AU - Romero, Pablo
AU - Abdelrahman, Omar
AU - Sudhalkar, Amruta
AU - Kusuma, Julius
AU - Cialdella, Ryan
AU - Garg, Nishant
AU - Varshney, Lav R.
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/6/29
Y1 - 2022/6/29
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - concrete
KW - sustainable building materials
KW - variational autoencoders
UR - http://www.scopus.com/inward/record.url?scp=85133858895&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133858895&partnerID=8YFLogxK
U2 - 10.1145/3530190.3534817
DO - 10.1145/3530190.3534817
M3 - Conference contribution
AN - SCOPUS:85133858895
T3 - ACM International Conference Proceeding Series
SP - 340
EP - 352
BT - Proceedings of the 4th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, COMPASS 2022
PB - Association for Computing Machinery
T2 - 4th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, COMPASS 2022
Y2 - 29 June 2022 through 1 July 2022
ER -