Artificial Neural Network Methodology for Soil Liquefaction Evaluation Using CPT Values

  • Ben-yu Liu
  • Liao-yuan Ye
  • Mei-ling Xiao
  • Sheng Miao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


With the 413 soil liquefaction records with cone penetration testing values collected after strong earthquakes, the Bayesian Regularization Back Propagation Neural Networks (BRBPNN) method was presented to evaluate the soil liquefaction potential in this paper. Cone resistance (q c ), equivalent dynamic shear stress (τ / σ0), mean grain size (D 50), total stress (σ 0), the effective stress (σ0), earthquake magnitude (M) and the normalized acceleration horizontal at ground surface (a / g) are used as input parameters for networks. Four networks are constructed for different source of input data. The model M7 seems more efficient for the given data, since it only contain 109 records. The model M5 contains 413 samples, and the correct ratio for training data and testing data are 88.5% and 90% respectively. By compared with the square of the weight of the input layer for each network, the importance order of the input parameters should be q c ,M,σ0,σ 0,a / g,τ / σ0 and D 50.


Back Propagation Neural Network Standard Penetration Test Cone Penetration Test Correct Ratio Soil Liquefaction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Shi, Z.J., Zhang, R.X., Gu, B.H.: Study on Synthetic Methods for Criteria And Appraisal of Sand Liquefaction. Earthquake Engineering and Engineering Vibration 17(1), 82–88 (1997) (in Chinese)Google Scholar
  2. 2.
    Li, F.M., Chen, G.X.: Saturated Sand Liquefaction Potential Estimation Method Based on BP Neural Network. Journal of Natural Disasters 14(2), 108–114 (2005) (in Chinese)Google Scholar
  3. 3.
    Goh, A.T.C.: Seismic Liquefaction Potential Assessed by Neural Networks. Journal of Geotechnical Engineering, ASCE 120(9), 1467–1480 (1995)CrossRefGoogle Scholar
  4. 4.
    Kua, C.S., Leeb, D.H., Wuc, J.H.: Evaluation of Soil Liquefaction in the Chi-Chi, Taiwan earthquake using CPT. Soil Dynamics and Earthquake Engineering 24(1), 659–673 (2004)CrossRefGoogle Scholar
  5. 5.
    Anthony, T.C.: Probabilistic Neural Network for Evaluating Seismic Liquefaction Potential. Canadian Geotechnical Journal 39, 219–232 (2002)CrossRefGoogle Scholar
  6. 6.
    Goh, A.T.C.: Neural-Network Modeling of CPT Seismic Liquefaction Data. Journal of Geotechnical Engineering, ASCE 122(1), 70–73 (1996)CrossRefGoogle Scholar
  7. 7.
    Juang, C.H., Chen, C.J., Tien, Y.M.: Appraising Cone Penetration Test Based Liquefaction Resistance Evaluation Methods: Artificial Neural Network Approach. Canadian Geotechnical Journal 36, 443–454 (1999)CrossRefGoogle Scholar
  8. 8.
    Seed, H.B., Idriss, I.M., Arango, I.: Evaluation of Liquefaction Potential Using Field Performance Data. Journal of Geotechnical Engineering, ASCE 109(3), 458–482 (1983)CrossRefGoogle Scholar
  9. 9.
    Stark, T.D., Olson, S.M.: Liquefaction Resistance using CPT and Field Case Histories. Journal of Geotechnical Engineering, ASCE 121(12), 856–869 (1995)CrossRefGoogle Scholar
  10. 10.
    Ke, H., Chen, Y.M.: An Improved Method for Evaluating Liquefaction Potential by the Velocity of Shear Waves. Acta Seismologica Sinica 22(6), 637–644 (2000)Google Scholar
  11. 11.
    Wang, M.W., Jin, J.L., Li, L.: Application of PP Method Based on Raga to Assessment of Sand Liquefaction Potential. Chinese Journal of Rock Mechanics and Engineering 23(4), 631–634 (2004)Google Scholar
  12. 12.
    MacKay, D.J.C.: Bayesian Interpolation. Neural Computation 4(3), 415–447 (1992)CrossRefGoogle Scholar
  13. 13.
    MacKay, D.J.C.: A practical Bayesian Framework for Back Propagation Networks. Neural Computation 4(3), 448–472 (1992)CrossRefGoogle Scholar
  14. 14.
    Foresee, F.D., Hagan, M.T.: Gauss-Newton Approximation to Bayesian Regularization. In: Proceedings of the 1997 International Joint Conference on Neural Networks, pp. 1930–1935 (1997)Google Scholar
  15. 15.
    Mahesh, P.: Support Vector Machines-based Modeling of sSeismic Liquefaction Potential. Int. J. Numer. Anal. Meth. Geomech. (2006),, doi:10.1002/nag.509
  16. 16.
    Tokimatsu, K., Yoshimi, Y.: Experimental Correlation of Soil Liquefaction Based on SPT N-Value and Fine Content. Soil and Foundations 23(4), 56–74 (1983)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ben-yu Liu
    • 1
  • Liao-yuan Ye
    • 1
  • Mei-ling Xiao
    • 1
  • Sheng Miao
    • 1
  1. 1.Institute of Public Safety and Disaster PreventionYunnan UniversityKunmingChina

Personalised recommendations