Data fusion technique for predicting soil classification

Patent metadata

Field	Value
Publication number	US-12007313-B2
Application number	US-201815977886-A
Country	US
Kind code	B2
Filing date	May 11, 2018
Priority date	May 17, 2017
Publication date	Jun 11, 2024
Grant date	Jun 11, 2024

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.

First claim

What is claimed is:

1. 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.

2. The method of claim 1 , wherein the soil classification is determined without laboratory equipment.

3. The method of claim 1 , further comprising the step of displaying graphically the soil classification.

4. The method of claim 1 , wherein the soil classification is a Unified Soil Classification System (USCS) classification.

5. The method of claim 1 , further comprising displaying the soil classification on a soil texture triangle.

6. The method of claim 1 , further comprising adding noise to the test samples that comprise one or more qualitative tests used to train a neural network to obtain the validation dataset that is applied to the tests of the soil samples.

7. 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.

8. The system of claim 7 , wherein the soil classification is determined without laboratory equipment.

9. The system of claim 7 , further comprising the step of displaying graphically the soil classification.

10. The system of claim 7 , wherein the soil classification is a Unified Soil Classification System (USCS) classification.

11. The system of claim 7 , further comprising displaying the soil classification on a soil texture triangle.

12. The system of claim 7 , further comprising adding noise to the test samples that comprise one or more qualitative tests used to train a neural network to obtain the validation dataset that is applied to the tests of the soil samples.

Assignees

• Univ Southern Methodist

Inventors

• Story Brett
• Sitton Jase
• De Jong Adam

Classifications

• G01N1/04 Primary

  in the solid state, e.g. by cutting · CPC title

• E04C1/00

  Building elements of block or other shape for the construction of parts of buildings (of relatively thin form E04C2/00; structural elongated elements designed for load-supporting E04C3/00, e.g. columns or pillars E04C3/30; manufacture or material of building bricks, stones, or the like B28, C03, C04; paving elements E01C; general building constructions E04B, e.g. walls E04B2/00, floors E04B5/00, roofs E04B7/00, ceilings E04B9/00; {roof coverings E04D; coverings for walls or ceilings E04F13/00; floorings E04F15/00;} structural elements specially designed for built-in conduit shafts E04F17/00; {elements for buildings for particular purposes E04H7/00}; special elements for building ovens or furnaces F24B, F27D) · CPC title

• G01N2203/0023

  Bending · CPC title

• G01N2203/0019

  Compressive · CPC title

• G01N2203/0003

  Steady · CPC title