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|Title:||Geotechnical reliability of dam and levee embankments|
|Authors:||Michigan State University. Department of Civil and Environmental Engineering.|
Wolff, Thomas F.
Levee seepage analysis
Monte Carlo simulation
Point estimate (PE) method
Slope stability analysis
Taylor’s Series finite difference (TSFD) method
|Publisher:||Geotechnical and Structures Laboratory (U.S.)|
Engineer Research and Development Center (U.S.)
|Series/Report no.:||ERDC/GSL CR ; 04-1.|
Abstract: For the past several years, the U.S. Army Corps of Engineers has been applying reliability analysis to the evaluation of existing water-resource structures and using the results as a basis for investment decision-making for rehabilitation projects. Although the current guidance is specifically directed toward navigation structures, and particularly their structural aspects, the Corps has begun applying reliability analysis to other problems, including geotechnical problems. Reliability analysis is a potentially complex technique; however, reasonable characterizations of reliability can be made using several approximate methods, such as the Taylor’s Series Finite Difference (TSFD) method, the point estimate (PE) method, and Monte Carlo simulation. The TSFD and PE methods, in the form presently used by the Corps, produce reasonable and consistent results for linear functions and random variables with small coefficients of variation. The most common studies done for embankment analysis and design, namely seepage analysis and slope stability analysis, typically involve nonlinear functions, and often involve variables with large coefficients of variation. Nonlinearity and large variations may be better accounted for by some refinements in the probabilistic models. The choice of approximation method in the probabilistic model leads to tradeoffs between accuracy and practicality. In this study several alternative approaches to these methods are compared and evaluated. In this study the probabilistic characterization of soil permeability and soil strength are reviewed, and examples are provided for judgmentally estimating the expected value and standard deviation for random variables using a spreadsheet approach. This report systematically considers the characterization of geotechnical random variables, considers the assumptions of probabilistic methods, and illustrates their application to prototype examples.
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