Using AI to make lower-carbon, faster-curing concrete

Using AI to make lower-carbon, faster-curing concrete - Engineering at Meta

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By Julius Kusuma , Sebastian Ament , Eytan Bakshy , Rebeca Ayala

Meta has developed an open-source AI tool to design concrete mixes that are stronger, more sustainable, and ready to build with faster—speeding up construction while reducing environmental impact.

The AI tool leverages Bayesian optimization, powered by Meta’s BoTorch and Ax frameworks, and was developed with Amrize and the University of Illinois Urbana-Champaign (U of I) to accelerate the discovery of high-performance, low carbon concrete.

Meta successfully deployed a concrete mix that was optimized with the AI tool at a data center construction site. Being open-sourced and freely available, the AI-tool could help increase the adoption and optimization of sustainable concrete mixes in the construction industry at large.

Low carbon concrete solutions are essential for advancing our goal of net zero emissions in 2030 . Concrete production is a major contributor to the embodied carbon emissions in data center construction and accounts for 8% of all global CO2 emissions , according to the World Economic Forum.

Conventionally, concrete is optimized for strength (28-day compressive strength) and cost. But modern constructions – including data centers – require concrete that is optimized for sustainability, curing speed, workability, and finishability as well.

Innovation in concrete formulations is difficult and slow. Compared to traditional concrete, current formulas for low carbon concrete face several challenges: slower curing speeds, issues with surface quality, and complications in supply chains when novel materials are involved.

But concrete suppliers can utilize AI to develop and scale innovative concrete mixes as drop-in replacements, accelerating the discovery and integration of sustainable materials for large-scale use.

By collaborating with Amrize — one of the world’s largest cement manufacturers and major concrete suppliers — and the University of Illinois Urbana-Champaign (U of I), we’ve developed an AI model and pipeline to accelerate the discovery of new concrete mixtures that meet traditional requirements alongside newer sustainability needs.

Our work with Amrize and U of I has already resulted in the successful design and deployment of AI-designed green concrete at our new data center in Rosemount, MN .

Meta’s AI model for green concrete

Designing concrete formulas is a complex, multi-objective problem. The designer must choose between various types and proportions of cement, lower-carbon supplementary cementitious materials (SCMs), water-to-binder ratios, coarse and fine aggregate types, and admixtures. SCMs’ impact on concrete performance varies by source location and seasonality, requiring long-term tests for validation. Finally, time-consuming tests taking days and weeks are needed to fully validate the performance of new mixes. Thus, it is important for the design process to be as efficient as possible.

There are several key ingredients often used in a sustainable concrete mix:

Cement is the “glue” that holds concrete together. It’s made from calcining limestone, clay, and other minerals in a high-temperature rotary kiln – the process which contributes significantly to CO2 emissions. The cement is then mixed with water, SCMs, aggregates, and admixtures at a ready mix plant to create concrete. When the cement paste hydrates and stiffens over time, it forms a hard, binding gel that gives concrete its strength.

Slag is a byproduct of steel production. It’s a molten waste material that’s cooled and ground into a fine powder. In concrete, slag helps reduce concrete’s embodied carbon by replacing cement, and improves long-term strength, durability, and resistance to external chemicals.

Fly ash is a type of industrial by-product from coal-fired power plants. It’s collected from the air pollution control systems and can be used as a substitute for some of the cement in concrete. Fly ash helps reduce the embodied carbon in concrete by replacing cement, and also improves its long-term strength, durability, and workability.

Fine aggregate , like sand, is smaller than coarse aggregate and fills in the gaps between the larger rocks or gravel. Sand helps to create a smooth, even surface, and improves the overall texture of the concrete.

Coarse aggregate refers to crushed stone or gravel that are added to concrete to provide bulk volume and load-bearing capacity, helping the concrete resist cracking and shrinkage.

Mixing these ingredients together in different proportions gives rise to concrete with varying strength and sustainability properties. The properties of each ingredient varies by origin and condition of manufacturing. Furthermore, some of the SCMs are declining in availability, necessitating the discovery and incorporation of novel materials for which little-to-no data is available. All of this adds to the challenges of concrete design. The goal of our approach is to optimize the trade-off between strength and sustainability.

Several key ingredients used to generate concrete mixes, clockwise from top left: fly ash, coarse aggregates, fine aggregate, and cement. An example of a low carbon concrete mix design, showing the relative amount of ingredients by weight. To accelerate the concrete mix design process, Meta developed an AI model for sustainable concrete using BoTorch and Ax , Meta’s open-source software for Bayesian optimization and adaptive experimentation , respectively. This model uses multi-objective Bayesian optimization algorithms to learn and optimize concrete compositions. The approach predicts compressive strength curves for different mixtures, optimizing short- and long-term strength properties and sustainability.

(For technical details of the model and optimization algorithm, see our technical report, “ Sustainable Concrete via Bayesian Optimization” and our open source SustainableConcrete repository with the associated data and code.)

The basis of the approach is a model that predicts the compressive strength curves associated with different concrete mixtures.

The figure below shows an example of two strength curve predictions, one associated with a concrete mix including pure portland cement (purple)—the most commonly used type of cement—and a second one where a part of the cement was substituted by fly ash…