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Modelling of shear stiffness of unsaturated fine grained soils at very small strains

Authors
  • Wong, K.S.
  • Mašín, D.
  • Ng, C.W.W.1, 2, 3, 4, 5, 6, 7, 8
  • 1 Department of Engineering Geology
  • 2 Institute of Hydrogeology
  • 3 Engineering Geology and Applied Geophysics
  • 4 Faculty of Science
  • 5 Charles University in Prague
  • 6 Department of Civil and Environmental Engineering
  • 7 The Hong Kong University of Science and Technology
  • 8 Clear Water Bay
Type
Published Article
Journal
Computers and Geotechnics
Publisher
Elsevier
Publication Date
Jan 01, 2013
Accepted Date
Oct 20, 2013
Volume
56
Pages
28–39
Identifiers
DOI: 10.1016/j.compgeo.2013.10.005
Source
Elsevier
Keywords
License
Unknown

Abstract

The shear modulus at very small strains (less than 0.001%) is an important parameter in the design of geotechnical structures subjected to static and cyclic loadings. Although numerous soil models are available for predicting shear modulus of saturated and dry soils, only a few ones can predict shear stiffness at very small strains of unsaturated soils correctly. In this study, a few unsaturated soil models are evaluated critically and compared with a newly developed model. This newly proposed model is verified by using measured shear modulus at very small strains for three different low plasticity fine grained soils available in the literature. It is found that this new model can predict shear modulus at very small strain resulting from an increase and a decrease in mean net stress at constant matric suction for low plasticity fine grained soils. Moreover, this model is able to give a reasonably good prediction on shear stiffness at very small strain during wetting of a collapsible unsaturated soil. In addition, the newly proposed model is illustrated to capture a consistent trend with experimental data of shear stiffness at very small strain for non-collapsible soils obtained during drying–wetting cycles. This evaluation revealed that the newly proposed model has better predictive capabilities than some earlier formulations of the same simplicity. In addition, the proposed model with fewer parameters has similar predictive capability as compared with a more complex model.

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