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A feedforward neural network was used to simulate the behaviour of soil samples in uniaxial strain conditions, i.e. to predict the oedometer test results only on the basis of the basic soil properties. The artificial neural network was trained using a database of 217 samples of different cohesive soils from various locations in Slovenia. Good agreement between the neural network predictions and laboratory test results was observed for the test samples. This study confirms the link between basic soil properties and soil stress-strain behaviour and demonstrates that the artificial neural network successfully predicts soil stiffness in uniaxial strain conditions. A comparison between the neural network prediction and empirical formulae shows that the neural network gives a more accurate as well as a more general solution to the problem.
A feedforward neural network was used to simulate the behaviour of soil samples in uniaxial strain conditions, i.e. to predict the oedometer test results only on the basis of the basic soil properties. The artificial neural network was trained using a database of 217 samples of different cohesive soils from various locations in Slovenia. Good agreement between the neural network predictions and laboratory test results was observed for the test samples. This study confirms the link between basic soil properties and soil stress-strain behaviour and demonstrates that the artificial neural network successfully predicts soil stiffness in uniaxial strain conditions. A comparison between the neural network prediction and empirical formulae shows that the neural network gives a more accurate as well as a more general solution to the problem.
Modelling soil behaviour in uniaxial strain conditions by neural networks
Advances in Engineering Software ; 32 ; 805-812
2001
8 Seiten, 10 Quellen
Aufsatz (Zeitschrift)
Englisch
MODELLING THE SOIL BEHAVIOUR IN UNIAXIAL STRAIN CONDITIONS BY NEURAL NETWORKS
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