20.500.12556/RUL-32125 Modelling soil behaviour in uniaxial strain conditions by neural networks 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 oedometer tests artificial neural network soil characteristics edometrski poskusi neuronske mreže umetne neuronske mreže lastnosti zemljin true false false Elsevier Angleški jezik Angleški jezik Neznano 2015-07-10 10:12:12 2015-07-10 10:12:12 2022-11-21 15:21:19 0000-00-00 00:00:00 2001 0 0 Str. 805-812 Vol. 32 Vol. 32 2001 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 624.131.37 0965-9978 10.1016/S0965-9978(01)00032-1 1475681 34540032 ASE_soil_ANN_n.pdf ASE_soil_ANN_n.pdf 1 7197CDA5C3DA4C4AEF7453EEBE60C6DC b6918ced67e34eb7608c82882a838fda5662dfa9af45fc31914ce93a417a2e90 0ff87f6d-a1ae-11eb-a523-00155dcfd717 20.500.12556/rul/665a6e02-65a8-4611-aed8-5887549efb64 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=85635 Fakulteta za gradbeništvo in geodezijo 0 0 0