About this Journal Submit a Manuscript Table of Contents
ISRN Soil Science
Volume 2012 (2012), Article ID 346439, 10 pages
Research Article

Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine

Centre for Disaster Mitigation and Management, VIT University, Vellore 632014, India

Received 9 December 2011; Accepted 17 January 2012

Academic Editors: W. Ding and Z. He

Copyright © 2012 Pijush Samui. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. B. Baecher, “Geotechnical error analysis,” Transportation Research Record, vol. 1105, pp. 23–31, 1986. View at Scopus
  2. J. P. Delhomme, “Spatial variability and uncertainty in groundwater flow parameters: a geostatistical approach,” Water Resources Research, vol. 15, no. 2, pp. 269–280, 1979. View at Scopus
  3. P. H. S. W. Kulatilake, “Probabilistic Potentiometric surface mapping,” Journal of Geotechnical & Geoenvironmental Engineering, vol. 115, no. 11, pp. 1569–1587, 1989.
  4. M. Soulie, P. Montes, and V. Silvestri, “Modelling spatial variability of soil parameters,” Canadian Geotechnical Journal, vol. 27, no. 5, pp. 617–530, 1990. View at Scopus
  5. P. Chiasson, J. Lafleur, M. Soulie, and K. T. Law, “Characterizing spatial variability of a clay by geostatistics,” Canadian Geotechnical Journal, vol. 32, no. 1, pp. 1–10, 1995. View at Scopus
  6. D. J. DeGroot, “Analyzing spatial variability of in situ soil properties,” in Proceedings of the Conference on Uncertainty in the Geologic Environment (UNCERTAINTY '96), vol. 85, pp. 210–238, 1996.
  7. R. B. Kulkarni, “Bayesian kriging in geotechnical problems,” in Geostatistics for Natural Resources Characterization, Part 2, NATO ASI Series, pp. 775–786, Reidel, Dordrecht, The Netherlands, 1983.
  8. A. M. Yaglom, Theory of Stationary Random Functions, Prentice-Hall, Englewood Cliffs, NJ, USA, 1962.
  9. P. Lumb, “Spatial variability of soil properties,” in Proceedings of the 2nd International Conference on Applications of Statistics and Probability in Civil Engineering, pp. 397–421, Aachen, Germany, 1975.
  10. E. H. Vanmarcke, “Probabilistic modeling of soil profiles,” Journal of Geotechnical and Geoenvironmental Engineering, vol. 103, no. 11, pp. 1227–1246, 1977. View at Scopus
  11. W. H. Tang, “Probabilistic evaluation of penetration resistances,” Journal of the Geotechnical Engineering Division, vol. 105, no. 14902, pp. 1173–1191, 1979. View at Scopus
  12. T. H. Wu and K. Wong, “Probabilistic soil exploration: case history,” Journal of the Geotechnical Engineering Division, vol. 107, no. 16764, pp. 1693–1711, 1981. View at Scopus
  13. A. Asaoka and D. Athanasiou-Grivas, “Spatial variability of the undrained strength of clays,” Journal of the Geotechnical Engineering Division, vol. 108, no. 5, pp. 743–756, 1982. View at Scopus
  14. E. H. Vanmarcke, Random Fields: Analysis and Synthesis, The MIT Press, Cambridge, Mass, USA, 1983.
  15. G. B. Baecher, “On estimating auto-covariance of soil properties,” in Proceedings of the 4th ASCE Joint Specialty Conference on Probabilistic Mechanics and Structural Reliability, vol. 110, pp. 214–218.
  16. H. S. W. Kulatilake and K. M. Miller, “A scheme for estimating the spatial variation of soil properties in three dimensions,” in Proceedings of the 5th International Conference on Application of Statistics and Probabilities in Soil and Structural Engineering, pp. 667–677, Vancouver, British Columbia, Canada, 1987.
  17. G. A. Fenton, “Random field characterization NGES data,” in Proceedings of the Workshop on Probabilistic Site Characterization at NGES, Seattle, Wash, USA, 1998.
  18. K. K. Phoon and F. H. Kulhawy, “Characterization of geotechnical variability,” Canadian Geotechnical Journal, vol. 36, no. 4, pp. 612–624, 1999. View at Scopus
  19. M. Uzielli, G. Vannucchi, and K. K. Phoon, “Random filed chracterisation of strees-normalised cone penetration testing parameters,” Geotechnique, vol. 55, no. 1, pp. 3–20, 2005.
  20. K. K. Phoon, S. T. Quek, and P. An, “Identification of statistically homogeneous soil layers using modified bartlett statistics,” Journal of Geotechnical and Geoenvironmental Engineering, vol. 129, no. 7, pp. 649–659, 2003. View at Publisher · View at Google Scholar · View at Scopus
  21. C. H. Juang, T. Jiang, and R. A. Christopher, “Three-dimensional site characterisation: neural network approach,” Geotechnique, vol. 51, no. 9, pp. 799–809, 2001. View at Publisher · View at Google Scholar · View at Scopus
  22. D. Park and L. R. Rilett, “Forecasting freeway link ravel times with a multi-layer feed forward neural network,” Computer Aided Civil and Infrastructure Engineering, vol. 14, pp. 358–367, 1999.
  23. V. Kecman, Leaming and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, The MIT Press, Cambridge, Mass, USA, 2001.
  24. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
  25. S. Mukherjee, E. Osuna, and F. Girosi, “Nonlinear prediction of chaotic time series using support vector machine,” in Proceedings of the 7th IEEE Signal Processing Society Workshop, pp. 511–519, IEEE, New York, NY, USA, 1997.
  26. K. R. Muller, A. Smola, G. Ratsch, B. Scholkopf, J. Kohlmorgen, and V. Vapnik, “Predicting time series with support vector machines,” in Proceedings of the International Conference on Artificial Neural Networks (ICANN '97), p. 999, Springer, Berlin, Germany, 1997.
  27. V. Vapnik, S. Golowich, and A. Smola, “Support method for function approximation regression estimation and signal processing,” in Advances in Neural Information Processing Systems, M. Mozer and T. Petsch, Eds., vol. 9, MIT Press, Cambridge, Mass, USA, 1997.
  28. E. Osuna, R. Freund, and F. Girosi, “An improved training algorithm for support vector machines,” in Proceedings of the 7th IEEE Workshop on Neural Networks for Signal Processing, pp. 276–285, IEEE, New York, NY, USA, 1997.
  29. S. Gunn, “Support vector machines for classification and regression,” Tech. Rep., University of Southampton, Southampton, UK, 1998, Image Speech and Intelligent Systems Research Group.
  30. B. Scholkopf, Support Vector Learning, R. Oldenbourg, Munich, Germany, 1997.
  31. B. P. Radhakrishna and R. Vaidyanadhan, Geology of Karnataka, Geological Society of India, Bangalore, India, 1997.
  32. H. B. Seed, K. Tokimatsu, L. F. Harder, and R. M. Chung, “Influence of SPT Procedures in soil liquefaction resistance evaluation,” Journal of Geotechnical Engineering, vol. 111, no. 12, pp. 1425–1445, 1985. View at Scopus
  33. A. W. Skempton, “Standard penetration test procedures,” Geotechnique, vol. 36, no. 3, pp. 425–557, 1986.
  34. C. O. Riggs, “American standard penetration test practice,” in Proceedings of the 14th PSC ASCE Insitu Tests in Geotechnical Engineering, vol. 124, pp. 949–967. View at Scopus
  35. P. K. Robertson and C. E. Wride, “Cyclic liquefaction and its evaluation based on the SPT and CPT,” in Proceedings of the NCEER Workshop on Evaluation of Liquefaction Resistance of Soils, 1998, Directed by T. L. Youd and I. M. Idriss.
  36. J. H. Schmertmann, “Statics of SPT,” Journal Geotechnical Engineering Division, vol. 105, no. 14573, pp. 655–670, 1979. View at Scopus
  37. L. Finn and C. Ventura, “Challenging issues in local microzonation,” in Proceedings of the 5th International Conference on Seismic Zonation, pp. 1554–1561, Nice, France, 1995.
  38. B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,” in Proceedings of the 5th Annual Workshop on Computational Learning Theory, D. Haussler, Ed., pp. 144–152, ACM Press, Pittsburgh, Pa, USA, 1992.
  39. K. P. Bennett and O. L. Mangasarian, “Robust linear programming discrimination of two linearly inseparable sets,” Optimization Methods and Software, vol. 1, no. 1, pp. 23–34, 1992. View at Scopus
  40. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Scopus
  41. A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and Computing, vol. 14, no. 3, pp. 199–222, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  42. V. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, NY, USA, 1998.
  43. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machine, Cambridge University Press, London, UK, 2000.