Investigation programs are intended to reduce uncertainty in site conditions that impact design performance. However, existing theoretical tools for assessing uncertainty reduction, such as Bayes' theorem, have seen limited practical use, because cumbersome numerical solutions are usually required. This paper describes a first-order, second-moment Bayesian method (FSBM) that overcomes this limitation. FSBM is a practical, analytical approximation to Bayes' theorem that helps estimate the reduction in uncertainty achieved for a particular investigation outcome (inverse analysis) and the reduction expected to result for proposed programs (design or forward analysis). In this initial application, FSBM is formulated and applied to updating uncertainty in the geometry of a single subsurface feature for one-dimensional search programs. Uncertainty in the existence of the feature is also addressed. FSBM provides solutions for example cases that are generally within 10% of solutions obtained by Monte Carlo simulation. The method is intended and shows promise for more general applications such as the design of 3D investigation programs or building probabilistic models of 3D subsurface features.

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  • Supplemental Notes:
    • This research was funded by the Gulf Coast Hazardous Substance Research Center (Project No. 104UTA0454), Houston, Texas, the National Science Foundation (CMS-9624544), Washington, D.C., and the Texas Higher Education Coordinating Board (ARP 302), Austin, Texas.
  • Corporate Authors:

    American Society of Civil Engineers

    1801 Alexander Bell Drive
    Reston, VA  United States  20191-4400
  • Authors:
    • McGrath, T C
    • Gilbert, R B
  • Publication Date: 1999-12


  • English

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Filing Info

  • Accession Number: 00779704
  • Record Type: Publication
  • Contract Numbers: 104UTA0454, CMS-9624544, ARP 302
  • Files: TRIS
  • Created Date: Dec 23 1999 12:00AM