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

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

Izvleček
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

Jezik:Angleški jezik
Ključne besede:oedometer tests, artificial neural network, soil characteristics
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FGG - Fakulteta za gradbeništvo in geodezijo
Založnik:Elsevier
Leto izida:2001
Št. strani:Str. 805-812
Številčenje:Vol. 32, Vol. 32
PID:20.500.12556/RUL-32125 Povezava se odpre v novem oknu
UDK:624.131.37
ISSN pri članku:0965-9978
DOI:10.1016/S0965-9978(01)00032-1 Povezava se odpre v novem oknu
COBISS.SI-ID:1475681 Povezava se odpre v novem oknu
Datum objave v RUL:10.07.2015
Število ogledov:3108
Število prenosov:1104
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Gradivo je del revije

Naslov:Advances in engineering software
Skrajšan naslov:Adv. eng. softw.
Založnik:Elsevier Applied Science
ISSN:0965-9978
COBISS.SI-ID:34540032 Povezava se odpre v novem oknu

Sekundarni jezik

Jezik:Angleški jezik
Ključne besede:edometrski poskusi, neuronske mreže, umetne neuronske mreže, lastnosti zemljin

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