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TitleUsing data mining techniques to predict deformability properties of jet grouting laboratory formulations over time
Author(s)Tinoco, Joaquim Agostinho Barbosa
Correia, A. Gomes
Cortez, Paulo
KeywordsGround improvement
Jet grouting
Young modulus
Artificial neutral networks
Support vector machines
Functional networks
Artificial Neural Networks
Issue dateOct-2011
JournalProgress in Artificial Intelligence
Abstract(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.
TypeConference paper
AccessRestricted access (UMinho)
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

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