  Eight percent of global carbon dioxide emissions can be attributed to the
production of cement, the main component of concrete, which is also the
dominant source of CO2 emissions in the construction of data centers. The
discovery of lower-carbon concrete formulae is therefore of high significance
for sustainability. However, experimenting with new concrete formulae is time
consuming and labor intensive, as one usually has to wait to record the
concrete's 28-day compressive strength, a quantity whose measurement can by its
definition not be accelerated. This provides an opportunity for experimental
design methodology like Bayesian Optimization (BO) to accelerate the search for
strong and sustainable concrete formulae. Herein, we 1) propose modeling steps
that make concrete strength amenable to be predicted accurately by a Gaussian
process model with relatively few measurements, 2) formulate the search for
sustainable concrete as a multi-objective optimization problem, and 3) leverage
the proposed model to carry out multi-objective BO with real-world strength
measurements of the algorithmically proposed mixes. Our experimental results
show improved trade-offs between the mixtures' global warming potential (GWP)
and their associated compressive strengths, compared to mixes based on current
industry practices. Our methods are open-sourced at
github.com/facebookresearch/SustainableConcrete.
