Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/96090
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dc.contributorDepartment of Civil and Environmental Engineeringen_US
dc.creatorLiu, WFen_US
dc.creatorLeung, YFen_US
dc.date.accessioned2022-11-07T03:36:53Z-
dc.date.available2022-11-07T03:36:53Z-
dc.identifier.issn0016-8505en_US
dc.identifier.urihttp://hdl.handle.net/10397/96090-
dc.language.isoenen_US
dc.publisherICE Publishingen_US
dc.rightsCopyright © ICE Publishing, all rights reserved.en_US
dc.rightsThis is the Author Manuscript of the work. The final published article is available at https://doi.org/10.1680/jgeot.16.P.336.en_US
dc.subjectIn situ testingen_US
dc.subjectSite investigationen_US
dc.subjectStatistical analysisen_US
dc.titleCharacterising three-dimensional anisotropic spatial correlation of soil properties through in situ test resultsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage805en_US
dc.identifier.epage819en_US
dc.identifier.volume68en_US
dc.identifier.issue9en_US
dc.identifier.doi10.1680/jgeot.16.P.336en_US
dcterms.abstractProper characterisations of soil properties and their variations are pivotal to the geotechnical design process. Although multiple in situ soil tests are routinely specified and performed in geotechnical investigation programmes, the information they provide regarding the spatial correlations of soil properties are often not fully utilised. This paper presents a holistic framework to characterise the three-dimensional, anisotropic, spatial variability of soil properties, using results of in situ tests such as standard penetration tests or vane shear tests. The restricted maximum likelihood method is implemented with an anisotropic covariance model, leading to improved predictive capabilities compared to conventional approaches, and allows quantification of the uncertainties on soil properties at unsampled locations, represented as distributions of prediction variance across the entire subsurface three-dimensional domain. The magnitudes of prediction variance at different locations can be used to provide guidance on the necessities and locations of additional soil sampling. They can also provide key input parameters for random field models in site-specific probabilistic analyses of geotechnical projects. The proposed approach is applied to the study of two project sites in Hong Kong, where it is shown that the three-dimensional spatial correlation features may be interpreted together with the geological settings at the site.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationGeotechnique, Sept. 2018, v. 68, no. 9, p. 805-819en_US
dcterms.isPartOfGeotechniqueen_US
dcterms.issued2018-09-
dc.identifier.scopus2-s2.0-85050697927-
dc.description.validate202211 bckwen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberRGC-B3-0744-
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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