Maximum Shear Modulus Prediction by Marchetti Dilatometer Test Using Neural Networks

  • Manuel Cruz
  • Jorge M. Santos
  • Nuno Cruz
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 363)


The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than was previously thought, and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels typically in the order of 10− 2 to 10− 4 of strain. Although the best approach seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice. In this work, a new approach using Neural Networks is proposed for sedimentary soils and the results are discussed and compared with some of the most common available methodologies for this evaluation.


Radial Basis Function Support Vector Regression Shear Wave Velocity Neural Network Radial Basis Function Sedimentary Soil 
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.


  1. 1.
    Clayton, C., Heymann, G.: Stiffness of geomaterials at very small strains. Géotechnique 51(3), 245–255 (2001)CrossRefGoogle Scholar
  2. 2.
    Fahey, M.: Soil stiffness values for foundation settlement analysis. In: Proc. 2nd Int. Conf. on Pre-failure Deformation Characteristics of Geomaterials, vol. 2, pp. 1325–1332. Balkema, Lisse (2001)Google Scholar
  3. 3.
    Peck, R.B., Hanson, W.E., Thornburn, T.H.: Foundation Engineering, 2nd edn. John Wiley & Sons, Chichester (1974)Google Scholar
  4. 4.
    Lunne, T., Robertson, P., Powell, J.: Cone penetration testing in geotechnical practice. Spon E & F N (1997)Google Scholar
  5. 5.
    Marchetti, S.: In-situ tests by flat dilatometer. Journal of the Geotechn. Engineering Division 106(GT3), 299–321 (1980)Google Scholar
  6. 6.
    Cruz, N., Devincenzi, M., Viana da Fonseca, A.: Dmt experience in iberian transported soils. In: Proc. 2nd International Flat Dilatometer Conference, pp. 198–204 (2006)Google Scholar
  7. 7.
    Cruz, N.: Modelling geomechanics of residual soils by DMT tests. PhD thesis, Universidade do Porto (2010)Google Scholar
  8. 8.
    Marchetti, S.: The flat dilatometer: Design applications. In: Third Geotechnical Engineering. Conf. Cairo University (1997)Google Scholar
  9. 9.
    Mayne, P.W.: Interrelationships of dmt and cpt in soft clays. In: Proc. 2nd International Flat Dilatometer Conference, pp. 231–236 (2006)Google Scholar
  10. 10.
    Hryciw, R.D.: Small-strain-shear modulus of soil by dilatometer. Journal of Geotechnical Eng. ASCE 116(11), 1700–1716 (1990)CrossRefGoogle Scholar
  11. 11.
    Hardin, B.O., Blandford, G.E.: Elasticity of particulate materials. J. Geot. Eng. Div 115(GT6), 788–805 (1989)CrossRefGoogle Scholar
  12. 12.
    Jamiolkowski, B.M., Ladd, C.C., Jermaine, J.T., Lancelota, R.: New developments in field and laboratory testing of soilsladd, c.c. In: XI ISCMFE, vol. 1, pp. 57–153 (1985)Google Scholar
  13. 13.
    Sully, J.P., Campanella, R.G.: Correlation of maximum shear modulus with dmt test results in sand. In: Proc. XII ICSMFE, pp. 339–343 (1989)Google Scholar
  14. 14.
    Tanaka, H., Tanaka, M.: Characterization of sandy soils using CPT and DMT. Soils and Foundations 38(3), 55–65 (1998)Google Scholar
  15. 15.
    Marchetti, S., Monaco, P., Totani, G., Marchetti, D.: In -situ tests by seismic dilatometer (SDMT). In: Crapps, D.K. (ed.) From Research to Practice in Geotechnical Engineering, vol. 180, pp. 292–311. ASCEGeotech. Spec. Publ. (2008)Google Scholar
  16. 16.
    Huang, Y., Draper, N.R.: Transformations, regression geometry and R2. Computational Statistics & Data Analysis 42(4), 647–664 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Cortes, C., Vapnik, V.: Support-vector Networks. Journal of Machine Learning 20(3), 273–297 (1995)zbMATHGoogle Scholar
  18. 18.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  19. 19.
    Schölkopf, B., Smola, A., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Computation 12, 1207–1245 (2000)CrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2011

Authors and Affiliations

  • Manuel Cruz
    • 1
    • 2
  • Jorge M. Santos
    • 1
    • 2
  • Nuno Cruz
    • 3
    • 4
  1. 1.ISEP - Instituto Superior de Engenharia do PortoPortugal
  2. 2.LEMA - Laboratório de Engenharia MatemáticaPortoPortugal
  3. 3.Mota-EngilPortugal
  4. 4.Universidade de AveiroPortugal

Personalised recommendations