  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. As such, there is interest in creating concrete formulas
that minimize this environmental burden, while satisfying engineering
performance requirements. Recent advances in artificial intelligence have
enabled machines to generate highly plausible artifacts, such as images of
realistic looking faces. Semi-supervised generative models allow generation of
artifacts with specific, desired characteristics. In this work, we use
Conditional Variational Autoencoders (CVAE), a type of semi-supervised
generative model, to discover concrete formulas with desired properties. Our
model is trained using open data from the UCI Machine Learning Repository
joined with environmental impact data computed using a web-based tool. We
demonstrate CVAEs can design concrete formulas with lower emissions and natural
resource usage while meeting design requirements. To ensure fair comparison
between extant and generated formulas, we also train regression models to
predict the environmental impacts and strength of discovered formulas. With
these results, a construction engineer may create a formula that meets
structural needs and best addresses local environmental concerns.
