Interpolating spatially varying soil property values from sparse data for facilitating characteristic value selection

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dc.citation.journalTitleCANADIAN GEOTECHNICAL JOURNAL
dc.contributor.authorZhao
dc.contributor.authorT.
dc.contributor.authorMontoya-Noguera
dc.contributor.authorS.
dc.contributor.authorPhoon
dc.contributor.authorK.-K.
dc.contributor.authorWang
dc.contributor.authorY.
dc.contributor.researchgroupMecánica Aplicadaspa
dc.date.accessioned2021-04-16T20:10:41Z
dc.date.available2021-04-16T20:10:41Z
dc.date.issued2018-02-01
dc.description.abstractLimit state design, incorporated into many recent geotechnical design codes, introduces the application of partial or resistance factors to selected characteristic values. Partial or resistance factors are usually set by national standard organizations, while characteristic values of geotechnical parameters are selected by engineers, often based on sparse measurement data combined with subjective engineering experience and judgment. Due to this subjective selection and individual judgment, the characteristic value derived by different engineers from the same dataset may vary greatly, especially when the test data contain significant variability. To address this issue, a new method based on Bayesian compressive sampling (BCS) is proposed in this study. BCS is able to reconstruct a high-resolution geotechnical property profile from sparse measurement data and quantify the uncertainty, e.g., confidence interval (CI) associated with the interpreted profile. The quantified uncertainty in the BCS has a clear statistical meaning: the corresponding confidence level for a CI from the BCS is the expected coverage proportion (i.e., fraction) of the complete profile that falls within the CI, if all data points along depth can be measured to provide the complete profile. This statistical meaning can be used to facilitate objective determination of characteristic values for geotechnical properties.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=7938
dc.identifier.doi10.1139/cgj-2017-0219
dc.identifier.issn12086010
dc.identifier.issn2677261spa
dc.identifier.otherWOS;000423748300002
dc.identifier.otherSCOPUS;2-s2.0-85041346734
dc.identifier.urihttp://hdl.handle.net/10784/29205
dc.language.isoengeng
dc.publisherCanadian Science Publishing
dc.publisher.departmentUniversidad EAFIT. Departamento de Ingeniería Mecánicaspa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85041346734&doi=10.1139%2fcgj-2017-0219&partnerID=40&md5=1fb9615e43a2959064d26a9782c8336d
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/0008-3674
dc.sourceCANADIAN GEOTECHNICAL JOURNAL
dc.subject.keywordreliability-based designeng
dc.subject.keywordBayesian compressive samplingeng
dc.subject.keywordcompressive sensingeng
dc.subject.keywordsparse measurement dataeng
dc.subject.keywordsite investigationeng
dc.titleInterpolating spatially varying soil property values from sparse data for facilitating characteristic value selectioneng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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