Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/13975
Título: | Using data mining techniques to predict deformability properties of jet grouting laboratory formulations over time |
Autor(es): | Tinoco, Joaquim Agostinho Barbosa Correia, A. Gomes Cortez, Paulo |
Palavras-chave: | Ground improvement Jet grouting Young modulus Regression Artificial neutral networks Support vector machines Functional networks Artificial Neural Networks |
Data: | Out-2011 |
Editora: | Springer |
Revista: | Progress in Artificial Intelligence |
Resumo(s): | Jet 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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/13975 |
ISBN: | 9783642247682 |
DOI: | 10.1007/978-3-642-24769-9_36 |
ISSN: | 0302-9743 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals C-TAC - Artigos em Revistas Internacionais DSI - Engenharia da Programação e dos Sistemas Informáticos |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Using Data Mining Techniques to Predict Deformability.pdf Acesso restrito! | artigo | 1,38 MB | Adobe PDF | Ver/Abrir |