Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/30962

TitleSupport vector machines applied to uniaxial compressive strength prediction of jet grouting columns
Author(s)Tinoco, Joaquim Agostinho Barbosa
Correia, A. Gomes
Cortez, Paulo
KeywordsData mining
Support vector machines
Sensitivity analysis
Soft-soil
Soil cement mixtures
Soil improvement
Jet grouting
Uniaxial compressive strength
Issue dateJan-2014
PublisherElsevier
JournalComputers and Geotechnics
CitationTinoco, J., Gomes Correia, A. and Cortez, P. (2014) Support Vector Machines Applied to Uniaxial Compressive Strength Prediction of Jet Grouting Columns. Computers and Geotechnics, 55, 132-140. http://dx.doi.org/10.1016/j.compgeo.2013.08.010
Abstract(s)Learning from data is a very attractive alternative to “manually” learning. Therefore, in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. This approach, supported on advanced statistics analysis, is usually known as Data Mining (DM) and has been applied successfully in different knowledge domains. In the present study, we show that DM can make a great contribution in solving complex problems in civil engineering, namely in the field of geotechnical engineering. Particularly, the high learning capabilities of Support Vector Machines (SVMs) algorithm, characterized by it flexibility and non-linear capabilities, were applied in the prediction of the Uniaxial Compressive Strength (UCS) of Jet Grouting (JG) samples directly extracted from JG columns, usually known as soilcrete. JG technology is a soft-soil improvement method worldwide applied, extremely versatile and economically attractive when compared with other methods. However, even after many years of experience still lacks of accurate methods for JG columns design. Accordingly, in the present paper a novel approach (based on SVM algorithm) for UCS prediction of soilcrete mixtures is proposed supported on 472 results collected from different geotechnical works. Furthermore, a global sensitivity analysis is applied in order to explain and extract understandable knowledge from the proposed model. Such analysis allows one to identify the key variables in UCS prediction and to measure its effect. Finally, a tentative step toward a development of UCS prediction based on laboratory studies is presented and discussed
Typearticle
URIhttp://hdl.handle.net/1822/30962
DOI10.1016/j.compgeo.2013.08.010
ISSN0266-352X
Publisher versionhttp://www.journals.elsevier.com/computers-and-geotechnics
Peer-Reviewedyes
AccessrestrictedAccess
Appears in Collections:ISISE - Artigos em Revistas Internacionais
CAlg - Artigos em revistas internacionais/Papers in international journals
C-TAC - Artigos em Revistas Internacionais

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