Application of data mining techniques in the estimation of the uniaxial compressive strength of jet grouting columns over time

Construction and Building Materials

Volumn 25, Issue 3, 2011, Pages 1257-1262

  Tinoco, Joaquim ^a   Gomes Correia, A ^a   Cortez, Paulo ^a  

^a UNIVERSITY OF MINHO   (Portugal)

Author keywords

Data mining; Ground improvement; Jet grouting; Uniaxial compressive strength

Indexed keywords

ANALYTICAL MODEL; COMPLEX RELATIONSHIPS; CONTRIBUTING FACTOR; DATA MINING TECHNIQUES; DATA-DRIVEN MODEL; DESIGN RULES; ENGINEERING PROJECT; GROUND IMPROVEMENT; JET GROUTING; NEW APPROACHES; RATIONAL MODELS; SOIL IMPROVEMENT; SOIL TREATMENTS; UNIAXIAL COMPRESSIVE STRENGTH;

CONCRETE CONSTRUCTION; DATA MINING; GROUTING; MATHEMATICAL MODELS; MODELS; MORTAR; SOILS;

COMPRESSIVE STRENGTH;

EID: 78650249179     PISSN: 09500618     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conbuildmat.2010.09.027     Document Type: Article

References (20)

1. H. GmbH The soilcret - jet grouting process Keller Publications 2005
2. J. Falcão, A. Pinto, and F. Pinto Cases histories of ground improvement solutions using jet-grouting. 2000
3. A.B. Padura Study of the soil consolidation using reinforced jet grouting by geophysical and geotechnical techniques Construct Build Mater 2008 10.1016/j.conbuildmat.2008.07.01
4. U. Fayyad, G. Piattetsky-Shapiro, and P. Smyth The KDD process for extracting useful knowledge from volumes of data Commun ACM 39 11 1996 27 34
5. R. Rezania, and A. Javadi A new genetic programming model for predicting settlement of shallow foundations Can Geotech 2007 1462 1473
6. E. Yusuf Artificial neural networks approach for swell pressure versus soil suction behavior Can Geotech 2007 1215 1223
7. S. Lai, and M. Serra Concrete strength prediction by means of neural network Construct Build Mater 11 2 1997 93 98
8. R. Kewley, M. Embrechts, and C. Breneman Data strip mining for the virtual design of pharmaceuticals with neural networks IEEE Trans Neural Networks 11 3 2000 668 679
9. Gomes Correia A, Valente T, Tinoco J, Falcão J, Barata J, Cebola D. et al. Evaluation of mechanical properties of jet-grouting columns using different test methods. In: 17th International conference on soil mechanics and geotechnical engineering; 2009. p. 2169-71.
10. M. Shibazaki State of practice of jet grouting Grouting and Ground Treatment. New Orleans 2003 198 217
11. P. Gazzarrini, M. Kokan, and S. Jungaro Case history of jet grouting in British Columbia Underpinning of CN Rail Tunnel in North Vancouver. The Grout Line 2005 47 54
12. Cortez P. Data mining with neural networks and support vector machines using the R/rminer tool. In: Perner P, editor. Advances in data mining, Proceedings of 10th industrial conference on data mining, lecture notes in artificial intelligence 6171, Berlin, Germany: Springer; July 2010. p. 572-83.
13. T. Hastie, R. Tibshirani, and J. Friedman The elements of statistical learning: data mining, inference, and prediction 2001 Springer-Verlag NY, USA
14. A. Smola, and B. Scholkopf A tutorial on support vector regression Stat Comput 14 3 2004 199 222
15. V. Cherkassy, and Y. Ma Practical selection of SVM parameters and noise estimation for SVM regression Neural Networks 17 1 2004 113 126
16. Normalisation, Comité Européen the. Eurocode 2: Design of concret structures - part 1-1: general rules and rules for buildings. Bruxelas: CEN; 2004.
17. P. Croce, and A. Flora Analysis of single-fluid jet grouting Géotechnique 50 6 2000 739 748
18. P. Cortez, M. Portelinha, S. Rodrigues, V. Cadavez, and A. Teixeira Lamb meat quality assessment by support vector machines Neural Process Lett 24 1 2006 41 51
19. M. Shibazaki, and H. Yoshida Constructing bottom barriers with jet grouting Int Containment Technol Conf Exhibition 1997
20. Yoshitake I, Mitsui T, Yoshikawa T, Ikeda A, Nakagawa K. An evaluation method of ground improvement by jet-grouting Tokyo; 2004.