In collaboration with researchers at the University of Illinois , Meta researchers have developed
a new AI model that optimizes concrete mixtures for sustainability as well as strength.
In early field testing, carbon emission was reduced by 40 percent, while strength requirements
were exceeded.
Cement in concrete accounts for approximately 8 percent of carbon emissions globally.
If successful, the impact of this work could reach well beyond data center construction,
as it applies more broadly to the general construction industry.
Data centers power the internet and make it possible for people to connect with the communities
that are central to their lives.
However, the construction and operation of these data centers can also be a source of
greenhouse gas emissions.
Last year, Meta achieved net zero emissions in their operations, and they are now planning
to achieve net zero emissions across their value chain by the end of 2030.
This commitment includes addressing the indirect environmental impacts of their business, such
as from the embodied carbon found in their buildings.
Embodied carbon includes the upstream and downstream emissions from the manufacturing,
transportation, maintenance, replacement, and decommissioning of building materials.
Among these building materials, concrete is a major contributor to the embodied carbon
of Meta data centers.
Concrete is the most widely used engineered material in the world, with billions of tons
produced annually.
Cement in concrete is a major source of carbon emissions globally, accounting for 8 percent
of carbon emissions.
Thus, reducing the emissions from concrete will have an impact that reaches well beyond
data center construction.
The researchers have found a way to do this by using artificial intelligence (AI) to generate
a concrete mix design that reduces the use of cement — without compromising strength
requirements.
Concrete is a complex mixture of dozens to hundreds of ingredients.
Conventional concrete is typically a mix of cement, construction aggregate, water, and
performance-enhancing chemicals known as admixtures.
There are many possible materials that can be used as aggregate, including gravel, crushed
stone, sand, slag, recycled concrete, and geosynthetic aggregates of different types
and sizes.
Since cement is the most carbon-intensive ingredient in concrete, one potential way
to reduce the carbon footprint of concrete is to reduce the amount of cement and/or replace
it with other cementitious materials like fly ash, slag, and ground glass, which have
a lower carbon intensity.
But getting the proportions of cement substitutes right is tricky when also trying to adhere
to stringent technical performance requirements.
Manually optimizing a concrete formula for both sustainable and technically sound outcomes
for different end uses is a formidable challenge, one that can be time-intensive.
Mathematically, each of the potentially hundreds of possible ingredients is an added dimension
in a complex space of all possible concrete formulas.
To efficiently and quickly optimize concrete for sustainable performance, and to generate
new concrete formulas, the researchers have turned to AI.
AI can be used to learn and optimize for specific outcomes within high dimensional spaces, where
data sets with many attributes can be modeled.
When a valid data set is available, AI can be used to estimate or “learn” the feasible
high-dimensional space in terms of relevant parameters, such as strength and sustainability.
To develop this type of model, the meta team leveraged the work of researchers at the University
of Illinois.
They trained a model using the Concrete Compressive Strength data set, openly available from the
UCI Machine Learning Repository.
This database has 1,030 instances of concrete formulas and their validated attributes, including
seven-day and 28-day compressive strength data (i.e., how the concrete gained strength
seven days and 28 days after pouring).
The embodied carbon footprint associated with the concrete formulas was derived using the
Cement Sustainability Initiative’s Environmental Product Declaration (EPD) tool.
EPDs are a standardized way of accounting for the environmental impacts of a product
or material, including carbon emissions over its life cycle.
Using the input data on concrete formulas along with their corresponding compressive
strength and carbon footprint, the AI model was able to generate a number of promising
new concrete mixes that could meet the stated data center requirements with a lower embodied
carbon impact than the industry standard.
That model generated a list of potential formulas, from which the researchers selected the five
best options.
They then further refined those to be used in the real world.
The researchers leveraged an iterative process: The potential formulas were tested in the
lab, refined, and retested.
This process was repeated until they identified formulas that could be handled and poured
easily, while also exceeding requirements for seven-day and 28-day strength with a 40
percent reduction in emissions.
Manually searching for concrete mixes that simultaneously optimize for multiple attributes,
like strength and sustainability, can be challenging, as technical performance has traditionally
been prioritized in the concrete industry, and there is a general perception that optimizing
for sustainability may adversely impact performance.
In the absence of AI resources, discovering similarly good, sustainable formulas that
also meet technical performance criteria could require several time-intensive iterations.
With AI, the researchers were able to accelerate the discovery process and validate good formulas
within weeks.
In this effort, the low-carbon concrete formulas generated by the model entailed significant
replacement of cement (upwards of 70 percent) with a combination of two types of low-carbon
substitutes, namely fly ash and slag.
The Meta researchers wanted to test the formulas in the field, and selected their data center
in DeKalb, Illinois, as an ideal location, given its proximity to the University of Illinois.
After the final formula was tested in labs, the researchers agreed to test them on multiple
(noncritical) structures at their DeKalb data center, namely the guardhouse floor slab and
the floor slab of the construction management team’s temporary office space building.
This refined concrete formula entailed upwards of 50 percent cement replacement with a combination
of fly ash and slag.
The field tests confirmed that the resulting low-emission concrete formula exceeded the
seven-day and 28-day strength requirements, with a carbon impact 40 percent lower than
the regional benchmark.
Depending on material availability, there is significant potential for the general building
industry to adopt the use of concrete mixes similar to the ones tested in data center,
particularly for construction projects that aren’t subject to compressed construction
schedules.
With the help of this AI model, the researchers have been able to successfully design and
use concrete that meets their long-term strength requirements and has a 40 percent lower carbon
impact than the regional benchmark.
While the researchers are encouraged by the success of this pilot, further developments
and tests are needed to scale the impact of this innovation.