US 12,442,723 B2
Data fusion technique for predicting soil classification
Brett Story, Rockwall, TX (US); Jase Sitton, Dallas, TX (US); and Adam De Jong, Swanzey, NJ (US)
Assigned to Southern Methodist University, Dallas, TX (US)
Filed by Southern Methodist University, Dallas, TX (US)
Filed on Jun. 10, 2024, as Appl. No. 18/738,222.
Application 18/738,222 is a continuation of application No. 15/977,886, filed on May 11, 2018, granted, now 12,007,313.
Claims priority of provisional application 62/507,677, filed on May 17, 2017.
Prior Publication US 2024/0337567 A1, Oct. 10, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. C04B 28/04 (2006.01); C04B 14/36 (2006.01); E04B 2/18 (2006.01); E04C 1/00 (2006.01); G01N 1/04 (2006.01); G01N 3/08 (2006.01); G01N 3/20 (2006.01); G01N 33/24 (2006.01)
CPC G01N 1/04 (2013.01) [C04B 14/361 (2013.01); C04B 28/04 (2013.01); E04B 2/18 (2013.01); E04C 1/00 (2013.01); G01N 33/24 (2013.01); G01N 3/08 (2013.01); G01N 3/20 (2013.01); G01N 2203/0003 (2013.01); G01N 2203/0019 (2013.01); G01N 2203/0023 (2013.01); G01N 2203/0067 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A method of making a compressed earth block (CEB) 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; 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
mixing a local soil, water, and a stabilizer into a CEB based on the calculated soil classification from the soil sample.