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Modelling soil behaviour in uniaxial strain conditions by neural networks
ID Turk, Goran (Author), ID Logar, Janko (Author), ID Majes, Bojan (Author)

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PID: 20.500.12556/rul/665a6e02-65a8-4611-aed8-5887549efb64

Abstract
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 basic soil properties. Artificial neural network was trained using the database of 217 samples of different cohesive soils from various location in Slovenia. Good agreement between neural network predictions and laboratory test results was observd 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 stiffnes 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

Language:English
Keywords:oedometer tests, artificial neural network, soil characteristics
Typology:1.01 - Original Scientific Article
Organization:FGG - Faculty of Civil and Geodetic Engineering
Publisher:Elsevier
Year:2001
Number of pages:Str. 805-812
Numbering:Vol. 32, Vol. 32
PID:20.500.12556/RUL-32125 This link opens in a new window
UDC:624.131.37
ISSN on article:0965-9978
DOI:10.1016/S0965-9978(01)00032-1 This link opens in a new window
COBISS.SI-ID:1475681 This link opens in a new window
Publication date in RUL:10.07.2015
Views:4554
Downloads:1202
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TURK, Goran, LOGAR, Janko and MAJES, Bojan, 2001, Modelling soil behaviour in uniaxial strain conditions by neural networks. Advances in engineering software [online]. 2001. Vol. 32, no. 32, p. 805–812. [Accessed 18 April 2025]. DOI 10.1016/S0965-9978(01)00032-1. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=32125
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Record is a part of a journal

Title:Advances in engineering software
Shortened title:Adv. eng. softw.
Publisher:Elsevier Applied Science
ISSN:0965-9978
COBISS.SI-ID:34540032 This link opens in a new window

Secondary language

Language:English
Keywords:edometrski poskusi, neuronske mreže, umetne neuronske mreže, lastnosti zemljin

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