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US 12,007,313 B2Granted
Data fusion technique for predicting soil classification
AssigneeSouthern Methodist University
InventorsBrett Story, Jase Sitton, Adam De Jong
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Abstract
Provided herein is a method for determining a soil classification comprising: obtaining a soil sample; conducting two or more field tests on the soil sample to obtain raw data for each field test; and calculating the soil classification from the raw data by applying a previously obtained validation dataset obtained from a training and validation soil classification calculation using samples of known soil classification, wherein the validation dataset is obtained using a feed-forward backpropagation neural network.
Claims12
A method of determining a soil classification for selecting a soil additive comprising:
- obtaining a soil sample;
- conducting two or more field tests on the soil sample to determine percent gravel, percent fines, and the plasticity index to obtain raw data for each of the two or more field tests, wherein at least one test is qualitative and one test if quantitative, wherein the qualitative field test is selected from at least one of pen, stick, and shine tests, and the quantitative test is selected from at least one of a wash test, a feel test, a test tube particle graduation, or a jar test;
- using a processor to train a neural network using at least two hidden layers to obtain a validation dataset that is applied to the soil sample tests of both quantitative and qualitative datasets;
- and
- calculating the soil classification from the raw data by applying the validation dataset obtained from a training and validation soil classification calculation of both quantitative and qualitative datasets using samples of known soil classification, wherein the validation dataset is obtained using a feed-forward backpropagation neural network, wherein the training occurs by adjustment of one or more synaptic weights, wherein the one or more synaptic weights are initialized as random numbers in a first pass of a training dataset that are input into the feed-forward backpropagation neural network and an output is generated, if this output does not match a target output within a predefined acceptable error, the weights and biases within the feed-forward backpropagation neural network are adjusted by reducing an error function related to a simulated output and the target output;
- and
- wherein the soil classification is within 10% of a known soil classification obtained using laboratory testing of a soil having any known soil classification;
- wherein the soil classification is used for selecting the soil additive used to prepare a mix for making a compressed earth block brick of a desired brick strength.
5 more elements
A system for determining a soil classification for selecting a soil additive comprising:
- a processor comprising a non-transitory computer readable medium comprising instructions stored thereon for:
- obtaining a soil sample;
- conducting two or more field tests on the soil sample to determine percent gravel, percent fines, and the plasticity index to obtain raw data for each of the two or more field tests, wherein at least one test is qualitative and one test if quantitative, wherein the qualitative field test is selected from at least one of pen, stick, and shine tests, and the quantitative test is selected from at least one of a wash test, a feel test, a test tube particle graduation, or a jar test;
- using a processor to train a neural network using at least two hidden layers to obtain a validation dataset that is applied to the soil sample tests of both quantitative and qualitative datasets;
- and
- calculating the soil classification from the raw data by applying the validation dataset obtained from a training and validation soil classification calculation of both quantitative and qualitative datasets using samples of known soil classification, wherein the validation dataset is obtained using a feed-forward backpropagation neural network, wherein the training occurs by adjustment of one or more synaptic weights, wherein the one or more synaptic weights are initialized as random numbers in a first pass of a training dataset that are input into the feed-forward backpropagation neural network and an output is generated, if this output does not match a target output within a predefined acceptable error, the weights and biases within the feed-forward backpropagation neural network are adjusted by reducing an error function related to a simulated output and the target output;
- and
- wherein the soil classification is within 10% of a known soil classification obtained using laboratory testing of a soil having any known soil classification;
- wherein the soil classification is used for selecting the soil additive used to prepare a mix for making a compressed earth block brick of a desired brick strength.
6 more elements
Dependencies are inferred from the claim text.