Abstract
Sometimes, the soil foundation is inadequate for constructions purpose (soft-soils). In these cases there is need to improve its mechanical and physical properties. For this purpose, there are several geotechnical techniques where Jet Grouting (JG) is highlighted. In many geotechnical structures, advance design incorporates the ultimate limit state (ULS) and the serviceability limit state (SLS) design criteria, for which uniaxial compressive strength and deformability properties of the improved soils are needed. In this paper, three Data Mining models, i.e. Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Functional Networks (FN), were used to estimate the tangent elastic Young modulus at 50% of the maximum stress applied (E tg50%) of JG laboratory formulations over time. A sensitivity analysis procedure was also applied in order to understand the influence of each parameter in E tg50% estimation. It is shown that the data driven model is able to learn the complex relationship between E tg50% and its contributing factors. The obtained results, namely the relative importance of each parameter, were compared with the predictive models of elastic Young modulus at very small strain (E 0) as well as the uniaxial compressive strength (Q u ). The obtained results can help to understand the behavior of soil-cement mixtures over time and reduce the costs with laboratory formulations.
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References
Castillo, E., Cobo, A., Gutirrez, J., Pruneda, R.: Functional networks with applications: a neural-based paradigm. Springer, Heidelberg (1999)
Cherkassky, V., Ma, Y.: Selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17(1), 113–126 (2004)
Gomes Correia, A., Valente, T., Tinoco, J., Falção, J., Barata, J., Cebola, D., Coelho, S.: Evaluation of mechanical properties of jet grouting columns using different test methods. In: Procedings of 17th International Conference on Soil Mechanics and Geotechnical Engineering, pp. 2179–2171 (2009)
Cortez, P.: Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool. In: Perner, P. (ed.) ICDM 2010. LNCS(LNAI), vol. 6171, pp. 572–583. Springer, Heidelberg (2010)
Croce, P., Flora, A.: Analysis of single-fluid jet grouting. Geotechnique 50(6), 739–748 (2001)
Falco, J., Pinto, A., Pinto, F.: Case histories of ground improvement solutions using jet grouting. Tecnasol FGE: Fundacoes e Geotecnia S. A (2000)
Goh, A.T.C., Goh, S.H.: Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data. Computers and Geotechnics 34(5), 410–421 (2007)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning II: Data Mining, Inference, and Prediction. Springer, Heidelberg (2009)
Kenig, S., Ben-David, A., Omer, M., Sadeh, A.: Control of properties in injection molding by neural networks. Engineering Applications of Artificial Intelligence 14(6), 819–823 (2001)
Kewley, R.H., Embrechts, M.J., Breneman, C.: Data strip mining for the virtual design of pharmaceuticals with neural networks. Neural Networks 11(3), 668–679 (2000)
Kirsch, F., Sondermann, W.: Ground improvement and its numerical analysis. In: Proceedings of the International Conference on Soil Mechanics and Geotechnical Engineering, vol. 3, pp. 1775–1778 (2001)
Padura, A.B., Sevilla, J.B., Navarro, J.G., Bustamante, E.Y., Crego, E.P.: Study of the soil consolidation using reinforced jet grouting by geophysical and geotechnical techniques. Construction and Building Materials 23(3), 1389–1400 (2009)
Shibazaki, M.: State of practice of jet grouting. In: Grouting and Ground Treatment, pp. 198–217 (2004)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Statistics and computing 14(3), 199–222 (2004)
Tinoco, J., Gomes Correia, A., Cortez, P.: A data mining approach for Jet Grouting Uniaxial Compressive Strength prediction. In: World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, pp. 553–558 (2009)
Tinoco, J., Gomes Correia, A., Cortez, P.: Applications of Data Mining Techniques in the estimations of Jet Grouting Laboratory Formulations over Time. In: 1st International Conference on Information Technology in Geo-Engineering, ICITG-Shanghai 2010, pp. 92–100 (2010)
GAMS Development Corporation. Online Documentation, Welcome to the GAMS Home Page, http://www.gams.com/docs/document.htm (accessed May 17, 2010)
Zhou, Y.Q., He, D.X., Nong, Z.: Application of Functional Network to Solving Classification Problems. Proceedings of the World Academy of Science, Engineering and Technology 7, 390–393 (2005)
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Tinoco, J., Correia, A.G., Cortez, P. (2011). Application of Data Mining Techniques in the Estimation of Mechanical Properties of Jet Grouting Laboratory Formulations over Time. In: Gaspar-Cunha, A., Takahashi, R., Schaefer, G., Costa, L. (eds) Soft Computing in Industrial Applications. Advances in Intelligent and Soft Computing, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20505-7_25
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DOI: https://doi.org/10.1007/978-3-642-20505-7_25
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