Machine learning concrete optimization

A system comprising:

• a data storage medium;
• and
• one or more computer hardware processors in communication with the data storage medium, wherein the one or more computer hardware processors are configured to execute computer-executable instructions to at least:
• receive one or more input parameters related to generating an artificial intelligence concrete mixture, the one or more input parameters comprising a cost and global warming potential objective;
• generate a plurality of candidate concrete mixtures;
• for each particular candidate concrete mixture from the plurality of candidate concrete mixtures,
• generate input data for the particular candidate concrete mixture;
• and
• invoke a machine learning model, wherein the machine learning model receives the input data as input, wherein the machine learning model outputs a respective prediction based on the input data;
• identify, from the plurality of candidate concrete mixtures, a filtered set of candidate concrete mixtures, wherein the filtered set of candidate concrete mixtures comprises (i) a first candidate concrete mixture and (ii) a second candidate concrete mixture, wherein identifying the filtered set of candidate concrete mixtures comprises:
• determining that a particular prediction for the particular candidate concrete mixture fails to satisfy a target performance threshold based on the one or more input parameters;
• apply an optimization function to the first candidate concrete mixture and the second candidate concrete mixture according to the cost and global warming potential objective, wherein the optimization function selects the first candidate concrete mixture over the second candidate concrete mixture;
• and
• provide the first candidate concrete mixture as the artificial intelligence concrete mixture.

A method comprising:

• generating a plurality of clusters from a plurality of concrete mixtures;
• selecting, from the plurality of clusters, a first subset of clusters, wherein one or more other clusters from the plurality of clusters are excluded from the first subset of clusters;
• creating, from the first subset of clusters, a first training data set;
• determining a first set of hyperparameters;
• training a first machine learning model using the first training data set and the first set of hyperparameters;
• validating the first machine learning model using the one or more other clusters;
• determining a second set of hyperparameters different from the first set of hyperparameters;
• and
• training a second machine learning model using a second training data set and the second set of hyperparameters.