Utilize este identificador para referenciar este registo: http://hdl.handle.net/1822/23463

TítuloJet grouting deformability modulus prediction using data mining tools
Autor(es)Tinoco, Joaquim Agostinho Barbosa
Correia, A. Gomes
Cortez, Paulo
DataSet-2012
EditoraTaylor and Francis
Resumo(s)Jet Grouting (JG) technology, one of the most efficient soft soils improvement methods, has been widely applied in important geotechnical works due to its versatility. However, there is still an important limitation to overcome related with the absence of rational approaches for its design. In the present work, three different Data Mining (DM) techniques, i.e., Artificial Neuronal Networks (ANN), Support Vector Machines (SVM) and multiple regression are trained in order to predict elastic young modulus (E0) of JG mixtures. It is shown that the complex relationships between E0 and its contributing factors can be learned using DM tools, particularly by SVM and ANN algorithms. By performing a detailed sensitivity analysis, understandable knowledge is extracted from the trained models, in terms of the relative importance of the attributes, as well as its effect in E0 prediction. In addition, the mathematical expression proposed by Eurocode 2 to estimate concrete stiffness, is adapted to JG material. Its low performance is assessed and compared with those achieved by DM models.
TipoconferencePaper
URIhttp://hdl.handle.net/1822/23463
ISBN978-0-415-62135-9
Arbitragem científicayes
AcessorestrictedAccess
Aparece nas coleções:CAlg - Artigos em livros de atas/Papers in proceedings

Ficheiros deste registo:
Ficheiro Descrição TamanhoFormato 
2012-5-IS-Hokkaido.pdf267,91 kBAdobe PDFVer/Abrir  Solicitar cópia ao autor!

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 Currículo DeGóis