This paper presents new developments of regression-based models to predict the saturated hydraulic conductivity of compacted soils from grain-size distribution. The models incorporate parameter values that adequately represent the distribution of grain sizes. Alternative representations of the grain-size distribution, the fractal dimension and entropy of the distributions, as well as porosity, soil density, and fines content are used in the models to estimate the hydraulic conductivity. These parameters that characterize the textural and hydraulic properties of the soil are combined and used in a multidimensional analysis to estimate the hydraulic conductivity. The predictions of the developed models are compared with those of existing models and laboratory measurements of hydraulic conductivity. The results suggest that the newly developed models outperform the existing models in predicting hydraulic conductivity using information from grain-size distribution. The presented models are suggested as alternatives to, for example, laboratory measurements of the hydraulic conductivity of certain soils that may be difficult to prepare or that may take several days or perhaps weeks to perform. In certain circumstances it may also be used to give first-hand information about the hydraulic properties in a field environment.

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  • Supplemental Notes:
    • Support for this research was provided by the National Science Foundation, Washington, D.C., grant CMS-98-13226 and from DuPont, Inc., Delaware.
  • Corporate Authors:

    American Society of Civil Engineers

    1801 Alexander Bell Drive
    Reston, VA  United States  20191-4400
  • Authors:
    • Boadu, F K
  • Publication Date: 2000-8


  • English

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Filing Info

  • Accession Number: 00795807
  • Record Type: Publication
  • Contract Numbers: CMS-9634193, CMS-9820832, CMS-98-13226
  • Files: TRIS
  • Created Date: Jul 31 2000 12:00AM