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)

Abstract

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.

Keywords

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.

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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

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