  Development of robust concrete mixes with a lower environmental impact is
challenging due to natural variability in constituent materials and a multitude
of possible combinations of mix proportions. Making reliable property
predictions with machine learning can facilitate performance-based
specification of concrete, reducing material inefficiencies and improving the
sustainability of concrete construction. In this work, we develop a machine
learning algorithm that can utilize intermediate target variables and their
associated noise to predict the final target variable. We apply the methodology
to specify a concrete mix that has high resistance to carbonation, and another
concrete mix that has low environmental impact. Both mixes also fulfill targets
on the strength, density, and cost. The specified mixes are experimentally
validated against their predictions. Our generic methodology enables the
exploitation of noise in machine learning, which has a broad range of
applications in structural engineering and beyond.
