Utilize este identificador para referenciar este registo: http://hdl.handle.net/1822/15291

TítuloA data mining approach for jet grouting uniaxial compressive strength prediction
Autor(es)Tinoco, Joaquim Agostinho Barbosa
Correia, A. Gomes
Cortez, Paulo
Palavras-chaveSoft soils
Soil-cement mixtures
Soil improvement
Jet grouting
Uniaxial compressive strength
Data mining
Support vector machines
Sensitivity analysis
Resumo(s)Uniaxial compressive strength (UCS) is the mechanical properties currently used in geotechnical works design, namely in jet grouting (JG) treatments. However, when working with this soil improvement technology, due to its inherent geological complexity and high number of variables involved, such design is a hard, perhaps very hard task. To help in such task, a support vector machine (SVM), which is a data mining algorithm particularly adequate to explore high number of complex data, was trained to estimate UCS of JG samples extracted from real JG columns. In the present paper, the performance reached by SVM algorithm in UCS estimation is shown and discussed. Furthermore, the relation between mixture porosity and volumetric content of cement and the JG system were identified as key parameters by performing a 1-D sensitivity analysis. In addition, the effect and the interaction between the key variables in UCS estimation was measured and analyzed.
Arbitragem científicayes
Aparece nas coleções:C-TAC - Comunicações a Conferências Internacionais
CAlg - Artigos em revistas internacionais/Papers in international journals
CAlg - Artigos em livros de atas/Papers in proceedings

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