Concrete-AI, a startup developed by engineering professors at the University of California at Los Angeles, is using artificial intelligence to predict the performance of concrete based on factors such as mixture proportion, type of coarse and fine aggregate, supplementary cementitious material and chemical admixture dosage. Other modules within the platform can help producers reduce embodied carbon and cost
Concrete mix technology is demanding and, all too often, thankless. We ask our people to guarantee the structural integrity of the design and minimize costs despite the host of bad things that can happen with mix components and transit. When things go right, our folks are invisible. Yet when things go wrong, they are the first to bear the brunt of the problem.
Now help is on the horizon via a new crop of applications based on machine learning, commonly referred to as artificial intelligence (AI). The premise is to use the vast history of mixes and material tests to optimize for cost or carbon dioxide (CO2)—and in many cases, both. Let’s take a closer look at one of the offerings, Concrete-AI.
Mathieu Bauchy, Ph.D., and Gaurav Sant, Ph.D., are professors at the University of California, Los Angeles (UCLA) Samueli School of Engineering and Applied Science. Several years ago, they started working with the Federal Highway Administration (FHWA) and a consortium of construction materials suppliers and concrete producers to find ways to reduce concrete’s carbon footprint through increased use of alternatives to portland cement.
The project was successful, and as a follow-up, they channeled their experience and knowledge of artificial intelligence into a new company dedicated to the optimization of concrete mixes: Concrete-AI.Concrete-AI is working with several concrete producers across North America and has analyzed more than 100,000 unique mixes with their associated material tests.
or each mix, the Concrete-AI engine attributed CO2 and cost to each component or grouping of materials at the plant and then “learned” the behavior of the mix as a function of its proportions. The engine now can take desired properties, such as strength and slump, and suggest the best mix design to minimize cost or CO2.