Application of ANN to Predict Liquefaction Potential of Soil Deposits for Chandigarh Region, India

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 258)


The phenomenon of liquefaction generally caused by dynamic factors where there is a mass of saturated soil sand. To prevent probable destruction of structures in such areas, prediction of liquefaction potential seems to be necessary. For the purpose of data collection we need to do boreholes at various locations and carry our many experiments, each of which requires a vast expenditure of time and money. Therefore, prediction of liquefaction by existing data leads us to decreasing cost of time and money. Neural networks are intelligent systems that uses specific processing characteristics of the brain The present study attempt to the prediction of liquefaction potential of soil deposits by artificial neural network approach in the Chandigarh region of India. To meet the objective 670 datasets from different boreholes were collected for the development of ANN models. ANN models were trained with seven input parameters by optimum number of hidden layers, epochs and suitable transfer function. Out of total datasets 70 % (470 datasets) of data were used for development of models and 30 % (200 datasets) of datasets were used for testing and validation. The predicted value of liquefaction potential by ANN models were compared with method [1], which shows that ANN method could predict with 95 % accuracy in Chandigarh region of India.


Artificial neural network Chandigarh region of India Liquefaction potential 



The authors are grateful to the Director, CSIR-Central Building Research Institute, Roorkee for giving the permission to publish the paper.


  1. 1.
    Youd, T.L. et al.: 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. ASCE 127(10), 817–833 (2001)Google Scholar
  2. 2.
    Hanna, A.M., Ural, D., Saygili, G.: Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dyn. Earthquake Eng. 27(2007), 521–540 (2007)CrossRefGoogle Scholar
  3. 3.
    Hanna, A.M., Ural, D., Saygili, G.: Evaluation of liquefaction potential of soil deposits using artificial neural networks, 2007. int. J. Comput. Aided. Eng. Software 24(1), 5–16 (2007)CrossRefGoogle Scholar
  4. 4.
    Goh, A.T.C.: Seismic liquefaction potential assessed by neural networks, 1994. J. Geotech. Eng. 120(9), 1467–1480 (1994)Google Scholar
  5. 5.
    Goh, A.T.C.: Pile driving records reanalyzed using neural networks. J Geotech Eng. ASCE 122(6), 492–500 (1996)CrossRefGoogle Scholar
  6. 6.
    Goh, A.T.C.: Probabilistic neural network for evaluating seismic liquefaction potential. Can. Geotech. J. 39, 219–232 (2002)Google Scholar
  7. 7.
    Barai, S., Agarwal, G.: Studies on instance based models for liquefaction potential assessment. Electron. J. Geotech. Eng. (2002) available at:
  8. 8.
    Juang, C.H., Chen, C.J., Tien, Y.M.: Appraising cone penetration test based liquefaction resistance evaluation method:artificial neural network approach. Can. Geotech. J. 36, 443–454 (1999)CrossRefGoogle Scholar
  9. 9.
    Juang, C.H., Chen, C.J., Jiang, T., Andrus, R.D.: Risk based liquefaction potential evaluation using standard penetration tests. Can. Geotech. J. 37, 1195–1208 (2000)Google Scholar
  10. 10.
    Baziar, M.H., Nilipour, N.: Evaluation of liquefaction potential using neural-networks and CPT results. Soil Dyn. Earthquake Eng. 23(2003), 631–636 (2003)CrossRefGoogle Scholar
  11. 11.
    Kumar, V., Venkatesh, K., Tiwari, R.P., Kumar,Y.: Applications of ANN to predict liquifaction potential. Int. J. Comput. Eng. Res. 2(2), 379–389 (2012)Google Scholar
  12. 12.
    Idriss, I.M., Boulanger, R.W.: Semi-empirical procedures for evaluating liquefaction potential during earthquakes. Soil Dyn. Earthquake Eng. 26(2006), 115–130 (2006)CrossRefGoogle Scholar
  13. 13.
    Dharmaraju, R., Ramakrishna, V.V.G.S.T., Gayatri D.: Liquefaction potential assessment of Chandigarh city—a conventional approach. In: A Workshop on Microzonation, ©Interline Publishing, BangaloreGoogle Scholar
  14. 14.
    Dharmaraju, R., Ramakrishna, V.V.G.S.T., Karthigeyan, S., Gayatri D.: Liquefaction potential of Chandigarh city—a conventional approach. In: The 12th International Conference of International Association for Computer Methods and Advances in Geomechanics (IACMAG), Goa, India, 1–6 Oct 2008Google Scholar
  15. 15.
    Abha, M., Dharmaraju, R., Gayatri, D.: Estimation of probable occurrence of earthquake in Chandigarh region, India. In: The 12th International Conference of International Association for Computer Methods and Advances in Geomechanics (IACMAG), Goa, India, 1–6 Oct 2008Google Scholar
  16. 16.
    Sitharam, T.G., Anbazhagan, P., Mahesh, G.U., Bharathi, K., Nischala Reddy, P.: ‘Seismic Hazard studies using Geotechnical Boerhole Data and GIS’. In: Proceedings of Symposium on Seismic Hazard Analysis and Microzonation, Roorkee, Sep 2005Google Scholar
  17. 17.
    Youd T. L., Idriss I. M. (eds.): NCEER Workshop on Evaluation of Liquefaction Resistance of Soils, 1997, National Center for Research on Earthquake Engineering, State University of New York, (1997)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Geotechnical Engineering GroupCSIR-Central Building Research InstittueRoorkeeIndia

Personalised recommendations