ESTIMATION FOR STOCHASTIC SOIL MODELS

Although considerable theory exists for the probabilistic treatment of soils, the ability to identify the nature of spatial stochastic soil variation is almost nonexistent. An entire site could be excavated, and there would be no doubt about the soil properties. However, there would no longer be anything to rest the structure on. Thus, uncertainty must be dealt with, and an attempt must be made to quantify it rationally. Twenty years ago, the mean and variance was sufficient. Clients are now demanding full reliablity studies, requiring more sophisticated models, so that engineers are becoming interested in rational soil correlation structures. Knowing that soil properties are spatially correlated, what is a reasonable correlation model? Are soils best represented using fractal models or finite-scale models? What is the difference? How can this question be answered? Once a model has been decided upon, how can its parameters be estimated? These are questions that this paper addresses by looking at a number of tools that aid in selecting appropriate stochastic models. These tools include the sample covariance, spectral density, variance function, variogram, and wavelet variance functions. Common models, corresponding to finite scale and fractal models, are investigated, and estimation techniques are discussed.

  • Availability:
  • Supplemental Notes:
    • The support of the National Sciences and Engineering Research Council of Canada under operating Grant OPG0105445 is acknowledged.
  • Corporate Authors:

    American Society of Civil Engineers

    1801 Alexander Bell Drive
    Reston, VA  United States  20191-4400
  • Authors:
    • Fenton, G A
  • Publication Date: 1999-6

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 00765066
  • Record Type: Publication
  • Contract Numbers: OPG0105445, 1434-93-G-2337
  • Files: TRIS
  • Created Date: Jun 19 1999 12:00AM