Abstract Scope |
With an annual production of 4 tons/capita, concrete is the second most used material in the world after water. Although concrete has largely defined modern society, it contributes to 8% of global CO2 emissions (quadruple the emissions of the entire aviation industry). In this presentation, I will discuss how AI can be used to reduce the carbon footprint of concrete. Based on a dataset of more than 1 million concrete mixtures, we trained a series of machine learning models that accurately predict the performance of a concrete formulation based on its mixture proportions. Based on these models, we introduced an inverse design engine that generates optimal concrete formulations featuring minimum carbon footprint while meeting all required performance targets and constraints. This approach results in an average reduction in concrete’s global warming potential of 30%—with no changes in the raw materials, no modification of the production process, and no cost premium. |