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Computational Geosciences

, Volume 12, Issue 4, pp 491–501 | Cite as

Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data, Gujarat, India

  • D. Ramakrishnan
  • T. N. Singh
  • N. Purwar
  • K. S. Barde
  • Akshay. Gulati
  • S. Gupta
Original paper

Abstract

This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.

Keywords

Liquefaction susceptibility Neural network Bhuj 

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References

  1. 1.
    Krinitzsky, E.L., Hynes, M.E.: The Bhuj, India earthquake: lessons learnt for earthquake safety of dams on alluvium. Eng. Geol. 58, 193–202 (2002)Google Scholar
  2. 2.
    Ramakrishnan, D., Mohanty, K.K., Nayak, S.R.: Mapping the liquefaction induced soil moisture changes using remote sensing technique: an attempt to map the earth induced liquefaction around Bhuj, Gujarat, India. Geotech. Geolog. Eng. 24, 1581–1602 (2006)CrossRefGoogle Scholar
  3. 3.
    Singh, R., Roy, D., Jain, S.K.: Analysis of earth dams affected by the 2001 Bhuj Earthquake. Eng. Geol. 80, 282–291 (2005)CrossRefGoogle Scholar
  4. 4.
    Malik, J.N., Sohoni, P.S., Karanth, R.V., Merh, S.S.: Modern and historic seismicity of Kachchh Peninsula, Western India. J. Geol. Soc. India 54, 545–550 (1999)Google Scholar
  5. 5.
    Sitharam, T.G., Govindaraju, L.: Geotechnical aspects and ground response studies in Bhuj earthquake, India. Geotech. Geolog. Eng. 22, 439–455 (2004)CrossRefGoogle Scholar
  6. 6.
    Ramakrishnan, D., Jeyaram, A., Mohanty, K.K., Nayak, S.R.: Mapping the liquefaction susceptible zones in parts of Kachchh region using IRS_WiFS and LISS-III data. In: Proceedings of the International Workshop on Earth System Process Related to Gujarat Earthquake Using Space Technology, pp. 27–29, 50–51. Department of Civil Engineering, IIT, Kanpur, India (2003)Google Scholar
  7. 7.
    Khandelwal, M., Roy, M.P., Singh, P.K.: Application of artificial neural network in mining industry. Indian Min. Eng. J. 43(7), 19–23 (2004)Google Scholar
  8. 8.
    Goh, A.T.C.: Empirical design in geotechnics using neural networks. Geotechnique 45(4), 709–714 (1995)MathSciNetGoogle Scholar
  9. 9.
    Goh, A.T.C.: Seismic liquefaction potential assessed by neural networks. J. Geotech. Geoenviron. Eng. 120(9), 1467–1480 (1995)Google Scholar
  10. 10.
    Teh, C.L., Wong, K.S., Goh, A.T.C., Jaritngam, S.: Predicting settlement of shallow foundations using neural networks. J. Comput. Civ. Eng. 11(2), 129–138 (1997)CrossRefGoogle Scholar
  11. 11.
    Goh, A.T.C.: Neural network modeling of CPT seismic liquefaction data. J Geotech. Eng. 122(1), 70–73 (1996)CrossRefGoogle Scholar
  12. 12.
    Ural, D.N., Saka, H.: Liquefaction assessment by neural networks. Elect. J. Geotech. Eng. http://geotech.civen.okstate.edu/ejge/ppr9803/index.html (1998)
  13. 13.
    Hanna, M.A., Ural, D., Saygili, G.: Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Int. J. Soil Dyn. Earthqu. Eng. 27(6), 521–540 (2007)CrossRefGoogle Scholar
  14. 14.
    Ishihara, K.: Liquefaction and flow failure during earthquakes. Geotechnique 43(3), 351–415 (1993)CrossRefGoogle Scholar
  15. 15.
    Chu, B.-L., Hsu, S.-C., Chang, Y.-M.: Ground behavior and liquefaction analyses in central Taiwan-Wufeng. Eng. Geol. 71, 119–139 (2003)CrossRefGoogle Scholar
  16. 16.
    Lee, D.-H., Ku, C.-S., Yuan, H.: A study of the liquefaction risk potential at Yuanlin, Taiwan. Eng. Geol. 71, 97–117 (2003)CrossRefGoogle Scholar
  17. 17.
    Yuan, H., Hiu Yang, S., Andrus, R.D., Hsein Juang, C.: Liquefaction induced ground failure: a study of the Chi-Chi earthquake cases. Eng. Geol. 17, 141–155 (2003)Google Scholar
  18. 18.
    Seed, H.B., Tokimatsu, K., Harder, L.F., Chung, R.M.: The influence of SPT procedures in soil liquefaction resistance evaluations. J. Geotech. Eng., ASCE. 111(12), 1425–1445 (1985), 16CrossRefGoogle Scholar
  19. 19.
    Youd, T.L., Idriss, I.M., Andrus, R.D., Arango, I., Castro, G., Christian, J.T., Dobry, R., LiamFinn, W.D., Harder, L.F. Jr, Hynes, M.E., Ishihara, K., Koester, J.P., Laio, S.S.C., Marcuson, W.F. III, Martin, G.R., Mitchell, J.K., Moriwaki, Y., Power, M.S., Robertson, P.K., Seed, R.B., Stokoe, K.H. II: Liquefaction resistance of soils: summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils. J. Geotech. Geoenviron. Eng. 127(10), 817–833 (2001)CrossRefGoogle Scholar
  20. 20.
    Seed, H.B., Idriss, I.M.: Simplified procedure for evaluating soil liquefaction potential. J. Soil Mech. Found Div., ASCE 97(SM9), 1249–1273 (1971), 14Google Scholar
  21. 21.
    Seed, H.B., Idriss, I.M., Arango, I.: Evaluation of liquefaction potential using field performance data. J. Geotech. Eng. 109, 458–482 (1983)Google Scholar
  22. 22.
    Youd, T.L., Perkins, D.M.: Mapping of liquefaction severity index. J. Geotech. Eng., ASCE. 113(11), 1374–1392 (1987), 17Google Scholar
  23. 23.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representation by Error Propagation in Parallel Distributed Processing. Massachusetts Institute of Technology Press, Cambridge (1986)Google Scholar
  24. 24.
    Lippmann, R.P.: An introduction to computing with neural nets. IEEE Trans. Acoust. Speech Signal Process. 42, 4–22 (1987)Google Scholar
  25. 25.
    Flood, I., Kartam, N.: Neural networks in civil engineering. I: Principles and understanding. J. Comput. Civ. Eng., ASCE, 82, 131–148 (1994)CrossRefGoogle Scholar
  26. 26.
    Xia, Y.Y., Xie, Y.M., Zhu, R.G.: An engineering geology evaluation method based on an artificial neural network and its application. Eng. Geol. 47, 149–156 (1997)CrossRefGoogle Scholar
  27. 27.
    Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing-Explorations in the Microstructure of Cognition 1/2. Massachusetts Institute of Technology Press, Cambridge (1986)Google Scholar
  28. 28.
    Widrow, B., Jovitz, M.C., Jacobi, G.T., Goldstein, G.: Generalization and information storage in networks of adaline neurons. In: Yovitz, M.C., Jacobi, G.T., Goldstein, G.D. (eds.) Self Organizing System, pp. 435–461. Spartan Books, Washington D.C. (1962)Google Scholar
  29. 29.
    Hecht-Neilsen, R.: Counterpropagation networks. Appl. Opt. 26(23), 4979–4984 (1987)Google Scholar
  30. 30.
    Powell, M.J.D.: Restart procedures for the conjugate gradient method. Math. Program. 12, 241–254 (1977)MATHCrossRefMathSciNetGoogle Scholar
  31. 31.
    Scales, L.E.: Introduction to Non-linear Optimization. Springer, New York (1985)Google Scholar
  32. 32.
    Gill, P.E., Murray, W., Wright, M.H.: Practical Optimization. Academic, New York (1981)MATHGoogle Scholar
  33. 33.
    Battiti, R.: First and second order methods for learning; between steepest descent and Newton’s method. Neural Comput. 4(2), 141–166 (1992)CrossRefGoogle Scholar
  34. 34.
    Singh, T.N., Verma, A.K., Sharma, P.K.: A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis. Geotech. Geolog. Eng. 25, 395–407 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • D. Ramakrishnan
    • 1
  • T. N. Singh
    • 1
  • N. Purwar
    • 2
  • K. S. Barde
    • 2
  • Akshay. Gulati
    • 1
  • S. Gupta
    • 2
  1. 1.Department of Earth SciencesIndian Institute of TechnologyPowaiIndia
  2. 2.Institute of TechnologyBanaras Hindu UniversityVaranasiIndia

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