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US 2023/0416164 A1Published application
Machine learning concrete optimization
AssigneeConcrete Ai, Inc.
InventorsMathieu Bauchy, Alexander Murray Hall
Life of this patent
Estimating the term…
Abstract
Artificial intelligence and machine learning models are used to make concrete-related predictions. Many permutations of concrete mixtures are generated. Machine learning algorithms are used to evaluate and recommend a generated concrete mixture based on a set of specifications. The generated concrete mixture can be sent to a plant for production. The actual concrete mixture that was used to manufacture the concrete product can be received from the manufacturer. An amount of emission reductions and/or cost savings can be determined from the actual as-batched concrete mixture and an associated reference concrete mixture. The real-world data are used to train the machine learning models.
Claims20
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;
- receive a first constraint on the artificial intelligence concrete mixture, wherein the first constraint comprises a threshold on a concrete mixture constituent;
- generate a plurality of candidate concrete mixtures;
- identify, from the plurality of candidate concrete mixtures, a subset of candidate concrete mixtures, wherein identifying the subset of candidate concrete mixtures comprises:
- determining that a candidate concrete mixture from the subset satisfies the threshold on the concrete mixture constituent;
- for each particular candidate concrete mixture from the subset 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 prediction based on the input data;
- identify, from the subset 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, 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.
14 more elements
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.
11 more elements
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.
6 more elements
Dependencies are inferred from the claim text.