Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/10824
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Tinoco, Joaquim Agostinho Barbosa | - |
dc.contributor.author | Correia, A. Gomes | - |
dc.contributor.author | Cortez, Paulo | - |
dc.date.accessioned | 2010-08-25T10:04:49Z | - |
dc.date.available | 2010-08-25T10:04:49Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | TINOCO, Joaquim; CORREIA, António Gomes; CORTEZ, Paulo - A Data Mining approach for Jet Grouting Uniaxial Compressive Strength Prediction. In ABRAHAM, Ajith [et al.], ed. lit. – “Proceedings of World Congress on Nature and Biologically Inspired Computing (NABIC 2009), Coimbatore, India, 2009” [Em linha]. [S.l.] : IEEE, cop. 2009. [Consult. 25 Ag. 2010]. Disponível em: http://dx.doi.org/10.1109/NABIC.2009.5393401. ISBN 978-1-4244-5612-3. | por |
dc.identifier.isbn | 978-1-4244-5612-3 | - |
dc.identifier.issn | 2164-7364 | por |
dc.identifier.uri | https://hdl.handle.net/1822/10824 | - |
dc.description.abstract | Jet Grouting (JG) is a Geotechnical Engineering technique that is characterized by a great versatility, being the best solution for several soil treatment improvement problems. However, JG lacks design rules and quality control. As the result, the main JG works are planned from empirical rules that are often too conservative. The development of rational models to simulate the effect of the different parameters involved in the JG process is of primary importance in order to satisfy the binomial safety-economy that is required in any engineering project. In this work, three data mining models, i.e. Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Functional Networks (FN), were adapted to predict the Uniaxial Compressive Strength (UCS) of JG laboratory formulations. A comparative study was held, by using a dataset used that was obtained from several studies previously accomplished in University of Minho. We show that the novel data-driven models are able to learn with high accuracy the complex relationships between the UCS of JG laboratory formulations and its contributing factors. | por |
dc.description.sponsorship | Tecnasol-FGE | por |
dc.language.iso | eng | por |
dc.publisher | IEEE | por |
dc.rights | openAccess | por |
dc.subject | Ground improvement | por |
dc.subject | Jet grouting | por |
dc.subject | Uniaxial compressive strength | por |
dc.subject | Artificial Neural Netwoks | por |
dc.subject | Data Mining | por |
dc.title | A data mining approach for jet grouting uniaxial compressive strength prediction | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/5393401 | - |
oaire.citationStartPage | 552 | por |
oaire.citationEndPage | + | por |
dc.identifier.doi | 10.1109/NABIC.2009.5393401 | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | World Congress on Nature and Biologically Inspired Computing | por |
sdum.conferencePublication | 2009 WORLD CONGRESS ON NATURE & BIOLOGICALLY INSPIRED COMPUTING (NABIC 2009) | por |
Aparece nas coleções: | C-TAC - Comunicações a Conferências Internacionais DSI - Engenharia da Programação e dos Sistemas Informáticos |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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2009-nabic.pdf | Documento principal | 177,78 kB | Adobe PDF | Ver/Abrir |