Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils

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dc.contributor.authorErzin, Y
dc.contributor.authorGumaste, SD
dc.contributor.authorGupta, AK
dc.contributor.authorSingh, DN
dc.date.accessioned2024-07-18T12:02:49Z
dc.date.available2024-07-18T12:02:49Z
dc.description.abstractThis study deals with development of artificial neural networks (ANNs) and multiple regression analysis (MRA) models for determining hydraulic conductivity of fine-grained soils. To achieve this, conventional falling-head tests, oedometer falling-head tests, and centrifuge tests were conducted on silty sand and marine clays compacted at different dry densities and moisture contents. Further, results obtained from ANN and MRA models were compared vis-a-vis experimental results. The performance indices such as the coefficient of determination, root mean square error, mean absolute error, and variance were used to assess the performance of these models. The ANN models exhibit higher prediction performance than the MRA models based on their performance indices. It has been demonstrated that the ANN models developed in the study can be employed for determining hydraulic conductivity of compacted fine-grained soils quite efficiently.
dc.identifier.issn0008-3674
dc.identifier.other1208-6010
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/8682
dc.language.isoEnglish
dc.publisherCANADIAN SCIENCE PUBLISHING
dc.subjectPEDO-TRANSFER FUNCTIONS
dc.subjectUNIAXIAL COMPRESSIVE STRENGTH
dc.subjectNATURAL SOFT CLAYS
dc.subjectFUZZY MODEL
dc.subjectELECTRICAL-RESISTIVITY
dc.subjectPEDOTRANSFER FUNCTIONS
dc.subjectWATER-CONTENT
dc.subjectPREDICTION
dc.subjectPERMEABILITY
dc.subjectCENTRIFUGE
dc.titleArtificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils
dc.typeArticle

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