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Determination of Coefficient of Lateral Pressure of Sandy Soil at Rest Using Results of Calibration of Cone Penetration Test and Artificial Neural Network

Besharat, Navid | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 45538 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Ahmadi, Mohammad Mehdi
  7. Abstract:
  8. The estimation of soil parameters in geotechnical practice is always an important and challenging task for the geotechnical engineer. Obtaining undisturbed samples from sands is generally very difficult and expensive, and in some cases impractical. A good prediction of sands parameters from insitu tests such as Cone Penetration Test (CPT) is one of the most challenging problems in geotechnical engineering. Using Calibration Chambers, a soil with predefined parameters is tested by cone penetrometer and some relationships are developed between CPT results and soil parameters. Using these relationships, in-situ results are interpreted.
    In this thesis, after introducing previously developed relationships, using a series of reliable CPT calibration chamber test data and a system consisting of three types of neural networks, the coefficient of lateral pressure of sandy soil at rest (K0) is predicted while it has a good agreement with measured data. In this system, a series of neural networks perform some tasks and finally by strategically combining of networks, the system will be able to predict parameter (K0) with reasonable accuracy. The proposed system uses Self Organizing Map (SOM) for clustering data into training, testing and validating sets and probabilistic neural networks for classifying of sands and back propagation neural networks for conclusive function approximation. Details on the development of such a system are described and finally results obtained by this system are compared to the available relations suggested by other researchers.
  9. Keywords:
  10. Cone Penetration Test ; Self-Organizing Map (SOM) ; Calibration Chamber ; Lateral Earth Pressure Coefficient ; Multilayered Neural Network ; Probabilistic Neural Networks

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