Semester

Fall

Date of Graduation

1999

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Civil and Environmental Engineering

Committee Chair

John P. Zaniewski.

Abstract

The ability of geotechnical engineers to accurately model slope performance is compromised by a variety of factors. The net result of these considerations is that the exact behavior of slopes cannot be accurately predicted. Hence, geotechnical engineers resort to a factor of safety approach to reduce the risk of slope failure. However, the factor of safety approach cannot quantify the probability of failure, or level of risk, associated with a particular design situation.;A probabilistic approach to studying geotechnical issues offers a systematic way to treat uncertainties, especially slope stability. In terms of probability, uncertainties can be related quantitatively to the design reliability of a slope. Therefore, the development of a risk-based design procedure, which engineers can use to combine practical experience, judgement, and statistical information is beneficial for analyzing the stability of a slope for an allowable risk criterion.;The objective of this research was to develop a probabilistic model for slope stability analysis. Through Monte Carlo simulation, the distribution of each input parameter is used with traditional behavior equations to produce a probability distribution of the output of the analysis. Allowable risk criterion is then applied to the output distribution to select the slope design parameters that have an acceptable level of risk.;To demonstrate the application of the probabilistic method developed during this research, the methodology was applied to two case studies. The first case study involved the factor of safety for an infinite slope without seepage analysis. The second case study obtained the critical height for a planar failure surface and the factor of safety for a circular failure surface using the response surface analysis method combined with a Monte Carlo simulation.

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