Modelling soil behaviour in uniaxial strain conditions by neural networks

Goran Turk in Janko Logar in Bojan Majes (2001) Modelling soil behaviour in uniaxial strain conditions by neural networks. Advances in engineering software, 32 (10-11). str. 805-812. ISSN 0965-9978

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The feed-forward 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. Artificial neural network was trained using the database of 217 samples of different cohesive soils from various locations in Slovenia. Good agreement between neural network predictions and laboratory test results was observed for the test samples. This study confirms the link between basic soil properties and stress-strain soil behaviour and demonstrates that artificial neural network successfully predicts soil stiffness in uniaxial strain conditions. The comparison between the neural network prediction and empirical formulae shows that the neural network gives more accurate as well as more general solution of the problem. (C) 2001 Civil-Comp Ltd and Elsevier Science Ltd. All rights reserved.

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