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dc.contributor.authorO'Kelly, Brendanen
dc.date.accessioned2023-09-18T12:38:58Z
dc.date.available2023-09-18T12:38:58Z
dc.date.created29 Aug - 1 Sept 2023en
dc.date.issued2023en
dc.date.submitted2023en
dc.identifier.citationXu S., Lai J., O'Kelly B.C. and Zhao B., Reverse extrusion test for fine-grained soil characterisation: internal flow pattern with ANN-enhanced particle tracking, Proceedings of the Fourth International Symposium on Machine Learning and Big Data in Geoscience, Cork, Ireland, 29 Aug - 1 Sept 2023, 2023, 3 (Extended Abstract #94)en
dc.identifier.otherYen
dc.descriptionPUBLISHEDen
dc.descriptionCork, Irelanden
dc.description.abstractThe reverse extrusion test involves one-dimensionally (1D) compressing a fine-grained soil sample contained in a cup container of cross-sectional area A. The force Fe applied by the loading platen causes extrusion of the soil to occur via a small and centrally located orifice on the platen. The applied force is continuously monitored during the test, with the pressure value causing extrusion (i.e., 𝑝e = 𝐹e 𝐴⁄ ) proposed as a means of quantifying the undrained shear strength and consistency limits of fine-grained soils [1]. Previous experimental results showed that the pe value correlates well with undrained shear strength at various water content for a specific soil type [2]. However, their relationship varies between soils and also depends on the compression velocity. Gas bubbles entrained during sample preparation and the complex internal flow pattern and possible localized sample consolidation that occur during the extrusion test [2] hinder understanding of the mechanism of the extrusion test. Thus, we adopted X-ray computed tomography (CT) combined with a recently developed marker-based tracking algorithm [3,4] to quantify the internal flow pattern of saturated kaolinite during extrusion tests.en
dc.format.extent3 (Extended Abstract #94)en
dc.language.isoenen
dc.rightsYen
dc.subjectShear strengthen
dc.subjectParticle trackingen
dc.subjectArtificial neural networken
dc.subjectExtrusion testen
dc.subjectFlow patternen
dc.titleReverse extrusion test for fine-grained soil characterisation: internal flow pattern with ANN-enhanced particle trackingen
dc.title.alternativeProceedings of the Fourth International Symposium on Machine Learning and Big Data in Geoscienceen
dc.typeConference Paperen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/bokellyen
dc.identifier.rssinternalid258642en
dc.rights.ecaccessrightsopenAccess
dc.subject.TCDThemeSmart & Sustainable Planeten
dc.subject.TCDTagARTIFICIAL NEURAL NETWORKSen
dc.subject.TCDTagEXTRUSIONen
dc.subject.TCDTagSOIL DEFORMATIONen
dc.subject.TCDTagSOIL PROPERTIESen
dc.subject.TCDTagSoil Mechanicsen
dc.subject.TCDTagfine grained soilen
dc.subject.TCDTaggeotechnicalen
dc.subject.TCDTagparticle trackingen
dc.subject.TCDTagshear strengthen
dc.subject.TCDTagsoil classificationen
dc.subject.TCDTagsoil plasticityen
dc.subject.TCDTagtracking algorithmen
dc.identifier.orcid_id0000-0002-1343-4428en
dc.status.accessibleNen
dc.identifier.urihttp://hdl.handle.net/2262/103894


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