Application of Artificial Neural Networks for the prediction of undrained shear modulus in cohesive soils

ce/papers ◽  
2018 ◽  
Vol 2 (2-3) ◽  
pp. 833-838
Author(s):  
Grzegorz WRZESIŃSKI ◽  
Zbigniew LECHOWICZ ◽  
Maria J. SULEWSKA
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Shear Modulus ◽  
Cohesive Soils ◽  
Artificial Neural ◽  
Undrained Shear

Estimation of Dynamic Properties of Sand Using Artificial Neural Networks

1996 ◽  
Vol 1526 (1) ◽  
pp. 1-5
Author(s):  
S. H. Ni ◽  
C. H. Juang ◽  
P. C. Lu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Shear Modulus ◽  
Dynamic Properties ◽  
Damping Ratio ◽  
Soil Parameters ◽  
Overburden Pressure ◽  
The Neural Network ◽  
Artificial Neural

Dynamic properties of soils are usually determined by time-consuming laboratory tests. This study presents a method for estimating dynamic soil parameters using artificial neural networks. A simple feedforward neural network with back-propagation training algorithm is used. The neural network is trained with actual laboratory data, which consists of six input variables. They are the standard penetration test value, the void ratio, the unit weight, the water content, the effective overburden pressure, and the mean effective confining pressure. The output layer consists of a single neuron, representing shear modulus or damping ratio. Results of the neural network training and testing show that predictions of shear modulus by the neural network approach is reliable although it is less successful in predicting damping ratio.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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