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dc.contributor.authorKrogstad, A.
dc.contributor.authorDepina, Ivan
dc.contributor.authorOmre, Henning
dc.date.accessioned2019-01-07T10:27:11Z
dc.date.available2019-01-07T10:27:11Z
dc.date.created2019-01-04T09:11:11Z
dc.date.issued2018
dc.identifier.issn1742-6588
dc.identifier.urihttp://hdl.handle.net/11250/2579431
dc.description.abstractThis study examines the application of the Hidden Markov model (HMM) to the soil classification based on Cone Penetration Test (CPT) measurements. The HMM is formulated in the Bayesian framework and composed of a Markov chain prior and a Gaussian likelihood model. The application of the Bayesian framework is considered as suitable because it allows for the integration of different sources of information commonly available in a CPT-based soil classification. The occurrence of different soil classes along a CPT profile is modeled with the Markov chain, while the Gaussian likelihood model establishes a relation between the different soil classes and CPT measurements. Preliminary performance of the HMM is examined on the classification of CPT measurements from the Sheringham Shoal Offshore Wind Farm.nb_NO
dc.language.isoengnb_NO
dc.publisherIOP Publishing Ltd.nb_NO
dc.relation.ispartofEERA DeepWind 2018
dc.relation.ispartof15th Deep Sea Offshore Wind R&D Conference
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectHidden Markov modelsnb_NO
dc.subjectOffshore oil well productionnb_NO
dc.subjectSoil surveysnb_NO
dc.subjectSoilnb_NO
dc.subjectBayesian frameworksnb_NO
dc.subjectCone penetrationnb_NO
dc.subjectBayesian inversionnb_NO
dc.subjectOffshore wind farmsnb_NO
dc.titleCone penetration data classification by Bayesian inversion with a Hidden Markov modelnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holderContent from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltdnb_NO
dc.subject.nsiVDP::Teknologi: 500nb_NO
dc.source.volume1104nb_NO
dc.source.journalJournal of Physics, Conference Seriesnb_NO
dc.source.issue012015nb_NO
dc.identifier.doi10.1088/1742-6596/1104/1/012015
dc.identifier.cristin1650091
cristin.unitcode7401,30,30,0
cristin.unitnameInfrastruktur
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal