Rapid soil classification using artificial neural networks for use in constructing compressed earth blocks

2017 ◽  
Vol 138 ◽  
pp. 214-221 ◽  
Author(s):  
Jase D. Sitton ◽  
Yasha Zeinali ◽  
Brett A. Story
Fibers ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 78
Author(s):  
Chiara Turco ◽  
Marco Francesco Funari ◽  
Elisabete Teixeira ◽  
Ricardo Mateus

The purpose of this study is to explore Artificial Neural Networks (ANNs) to predict the compressive and tensile strengths of natural fibre-reinforced Compressed Earth Blocks (CEBs). To this end, a database was created by collecting data from the available literature. Data relating to 332 specimens (Database 1) were used for the prediction of the compressive strength (ANN1), and, due to the lack of some information, those relating to 130 specimens (Database 2) were used for the prediction of the tensile strength (ANN2). The developed tools showed high accuracy, i.e., correlation coefficients (R-value) equal to 0.97 for ANN1 and 0.91 for ANN2. Such promising results prompt their applicability for the design and orientation of experimental campaigns and support numerical investigations.


2020 ◽  
Vol 401 ◽  
pp. 38-54 ◽  
Author(s):  
M.C. Pegalajar ◽  
L.G.B. Ruiz ◽  
M. Sánchez-Marañón ◽  
L. Mansilla

Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González

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