Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/13975

Full metadata record
DC FieldValueLanguage
dc.contributor.authorTinoco, Joaquim Agostinho Barbosa-
dc.contributor.authorCorreia, A. Gomes-
dc.contributor.authorCortez, Paulo-
dc.date.accessioned2011-10-24T11:08:30Z-
dc.date.available2011-10-24T11:08:30Z-
dc.date.issued2011-10-
dc.identifier.isbn9783642247682por
dc.identifier.issn0302-9743por
dc.identifier.urihttp://hdl.handle.net/1822/13975-
dc.description.abstractJet Grouting (JG) technology is one of the most used softsoil improvements methods. When compared with other methods, JG is more versatile, since it can be applied to several soil types (ranging from coarse to fine-grained soils) and create elements with different geometrics shapes (e.g. columns, panels). In geotechnical works where the serviceability limit state design criteria is required, deformability properties of the improved soil need to be quantified. However due to the heterogeneity of the soils and the high number of variables involved in the JG process, such design is a very complex and hard task. Thus, in order to achieve a more rational design of JG technology, this paper proposes and compares three data mining techniques in order to estimate the different moduli that can be defined in an unconfined compressed test of JG Laboratory Formulations (JGLF). In particular we analyze and discuss the predictive capabilities of Artificial Neural Networks, Support Vector Machines or Functional Networks. Furthermore, the key parameters in modulus estimation are identified by performing a 1-D sensitivity analysis procedure. We also analyze the effect of such variables in JGLF behavior.por
dc.description.sponsorshipThe authors wish to thank to Portuguese Foundation for Science and Technology (FCT) the support given through the doctoral grant SFRH/BD/45781/2008. Also, the authors would like to thank the interest and financial support by Tecnasol-FGE.por
dc.language.isoengpor
dc.publisherSpringerpor
dc.rightsrestrictedAccesspor
dc.subjectGround improvementpor
dc.subjectJet groutingpor
dc.subjectYoung moduluspor
dc.subjectRegressionpor
dc.subjectArtificial neutral networkspor
dc.subjectSupport vector machinespor
dc.subjectFunctional networkspor
dc.subjectArtificial Neural Networkspor
dc.titleUsing data mining techniques to predict deformability properties of jet grouting laboratory formulations over timepor
dc.typeconferencePaper-
dc.peerreviewedyespor
sdum.publicationstatuspublishedpor
oaire.citationStartPage491por
oaire.citationEndPage504por
oaire.citationIssue1por
oaire.citationTitleProgress in Artificial Intelligencepor
oaire.citationVolume7026por
dc.identifier.doi10.1007/978-3-642-24769-9_36por
dc.subject.wosScience & Technologypor
sdum.journalProgress in Artificial Intelligencepor
sdum.conferencePublicationPROGRESS IN ARTIFICIAL INTELLIGENCEpor
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals
C-TAC - Artigos em Revistas Internacionais
DSI - Engenharia da Programação e dos Sistemas Informáticos

Files in This Item:
File Description SizeFormat 
Using Data Mining Techniques to Predict Deformability.pdf
  Restricted access
artigo1,38 MBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID