# US 11,897,818 B2 · Machine learning concrete optimization

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- Number: US 11,897,818 B2
- Document ID: US11897818B2
- Status: Granted
- Kind: B2
- Application number: 18/318,568
- Filed: 2023-05-16
- Published: 2023-12-28
- Granted: 2024-02-13
- Earliest claimed: 2022-05-17
- Assignees: Concrete Ai, Inc.
- Applicants: Concrete Ai, Inc.
- Inventors: Mathieu Bauchy; Alexander Murray Hall
- Web page: https://patentgrep.com/patent/US11897818B2

## CPC

- C04B 40/0032: {Controlling the process of mixing, e.g. adding ingredients in a quantity depending on a measured or desired value (B28C7/00 takes precedence)}
- G06N 3/08: Learning methods
- G06F 30/27: using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
- G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning
- G06Q 10/06375: {Prediction of business process outcome or impact based on a proposed change}
- G06T 11/26: Drawing of charts or graphs
- G16C 20/20: Identification of molecular entities, parts thereof or of chemical compositions
- G16C 20/70: Machine learning, data mining or chemometrics
- C04B: LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE {(roofing granules E04D7/005)}; CERAMICS (devitrified glass-ceramics C03C10/00); REFRACTORIES; TREATMENT OF NATURAL STONE
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G16C: COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE

## 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.

## Claims

### Claim 1

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.

### Claim 2 (depends on 1)

The system of claim 1, wherein the one or more computer hardware processors are further configured to execute computer-executable instructions to at least:
calculate a coarseness factor value for the first candidate concrete mixture;
calculate a workability factor value for the first candidate concrete mixture; and
present, in a graphical user interface, a Shilstone visualization comprising a point in the Shilstone visualization representing the coarseness factor value and the workability factor value.

### Claim 3 (depends on 1)

The system of claim 1, wherein the one or more computer hardware processors are further configured to execute computer-executable instructions to at least:
determine an expected retention value for the first candidate concrete mixture for a particular sieve size; and
present, in a graphical user interface, a tarantula visualization comprising a point in the tarantula visualization representing the expected retention value for the particular sieve size.

### Claim 4 (depends on 1)

The system of claim 1, wherein the one or more input parameters comprise the target performance threshold.

### Claim 5 (depends on 1)

The system of claim 1, wherein the target performance threshold corresponds to at least one of a strength threshold, a slump threshold, or a shrinkage threshold.

### Claim 6 (depends on 1)

The system of claim 1, wherein the one or more input parameters comprise a reference concrete mixture, and wherein the one or more computer hardware processors are further configured to execute computer-executable instructions to at least:
generate reference input data for the reference concrete mixture; and
invoke the machine learning model, wherein the machine learning model receives the reference input data as input, wherein the machine learning model outputs a reference prediction based on the reference input data, wherein the target performance threshold is based on the reference prediction.

### Claim 7

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.

### Claim 8 (depends on 7)

The system of claim 7, wherein generating input data for the particular candidate concrete mixture comprises:
determining a first feature corresponding to a water-to-cementitious material ratio for the particular candidate concrete mixture;
determining a second feature corresponding to an aggregate density value for the particular candidate concrete mixture;
determining a third feature corresponding to an aggregate water absorption value for the particular candidate concrete mixture;
determining a fourth feature corresponding to an aggregate fineness modulus value for the particular candidate concrete mixture;
determining a fifth feature for an amount of a concrete mixture constituent in the particular candidate concrete mixture; and
converting the first feature, second feature, third feature, fourth feature, and fifth feature to vector data, wherein the input data comprises the vector data.

### Claim 9 (depends on 7)

The system of claim 7, wherein generating the plurality of candidate concrete mixtures comprises:
creating the first candidate concrete mixture comprising a plurality of concrete mixture constituents;
assigning a first value for a first concrete mixture constituent in the plurality of concrete mixture constituents for the first candidate concrete mixture;
adding the first candidate concrete mixture to the plurality of candidate concrete mixtures;
combining the first value and a step value to result in a second value;
creating a second candidate concrete mixture comprising the plurality of concrete mixture constituents;
assigning the second value for the first concrete mixture constituent for the second candidate concrete mixture; and
adding the second candidate concrete mixture to the plurality of candidate concrete mixtures.

### Claim 10 (depends on 7)

The system of claim 7, wherein generating the plurality of candidate concrete mixtures comprises:
creating a second candidate concrete mixture comprising a plurality of concrete mixture constituents;
determining a value associated with the second candidate concrete mixture;
determining that the value associated with the second candidate concrete mixture fails to satisfy a domain threshold; and
excluding the second candidate concrete mixture from the plurality of candidate concrete mixtures.

### Claim 11 (depends on 7)

The system of claim 7, wherein the one or more input parameters comprise a reference concrete mixture.

### Claim 12 (depends on 11)

The system of claim 11, wherein the one or more computer hardware processors are further configured to execute computer-executable instructions to at least:
calculate a first performance metric associated with the first candidate concrete mixture;
calculate a second performance metric associated with the reference concrete mixture; and
cause presentation, in a graphical user interface, of a visualization comprising the first performance metric and the second performance metric.

### Claim 13 (depends on 7)

The system of claim 7, wherein the one or more computer hardware processors are further configured to execute computer-executable instructions to at least:
generate first input data for the first candidate concrete mixture;
for each particular machine learning model from a plurality of machine learning models,
invoke the particular machine learning model, wherein the particular machine learning model receives the first input data as input; and
apply a statistical measure to output from each particular machine learning model from the plurality of machine learning models, wherein application of the statistical measure outputs a first prediction.

### Claim 14 (depends on 13)

The system of claim 13, wherein the one or more computer hardware processors are further configured to execute computer-executable instructions to at least:
calculate a confidence interval from the output from each particular machine learning model from the plurality of machine learning models, wherein identifying the filtered set of candidate concrete mixtures comprises:
determining that the first prediction combined with the confidence interval satisfies the target performance threshold.

### Claim 15

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.

### Claim 16 (depends on 15)

The method of claim 15, wherein generating the plurality of clusters comprises:
applying a K-means clustering algorithm to the plurality of concrete mixtures.

### Claim 17 (depends on 15)

The method of claim 15, wherein creating the first training data set comprises:
adding a label to the first training data set, wherein the label corresponds to at least one of: a strength value, a slump value, or a shrinkage value.

### Claim 18 (depends on 15)

The method of claim 15 comprising:
selecting, from the plurality of clusters, a second subset of clusters different from the first subset of clusters; and
creating, from the second subset of clusters, the second training data set.

### Claim 19 (depends on 15)

The method of claim 15, wherein validating the first machine learning model comprises:
generate input data for a concrete mixture from the one or more other clusters;
invoke the first machine learning model, wherein the first machine learning model receives the input data as input, wherein the first machine learning model outputs a prediction based on the input data; and
compare the prediction to a metric associated with the concrete mixture from the one or more other clusters.

### Claim 20 (depends on 15)

The method of claim 15, wherein the first set of hyperparameters comprises at least one of a number of neurons, a number of layers, a number of training epochs, an activation function, an optimizer, a learning rate, a batch size, or a regularization parameter.