摘要 :
This paper presents a probabilistic failure analysis approach that uses failure samples generated in Monte Carlo Simulation (MCS) and analyzes these failure samples to assess effects of various uncertainties on slope failure proba...
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This paper presents a probabilistic failure analysis approach that uses failure samples generated in Monte Carlo Simulation (MCS) and analyzes these failure samples to assess effects of various uncertainties on slope failure probability. The probabilistic failure analysis approach contains two major components: hypothesis tests for prioritizing effects of various uncertainties and Bayesian analysis for further quantifying their effects. The approach is implemented in a spreadsheet environment and applied to analyze the 1999 Shek Kip Mei landslide in Hong Kong. Hypothesis tests show that the uncertainty of pore water pressure ratio has the most significant effect on slope performance and its effect is further quantified by Bayesian analysis. Bayesian analysis results are validated against independent sensitivity studies. It is shown that probabilistic failure analysis provides results that are equivalent to those from additional sensitivity studies, but it avoids additional computational times and efforts for repeated runs of MCS in sensitivity studies.
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摘要 :
This paper presents a probabilistic failure analysis approach that uses failure samples generated
in Monte Carlo Simulation (MCS) and analyzes these failure samples to assess effects of various uncertainties
on slope failure pro...
展开
This paper presents a probabilistic failure analysis approach that uses failure samples generated
in Monte Carlo Simulation (MCS) and analyzes these failure samples to assess effects of various uncertainties
on slope failure probability. The probabilistic failure analysis approach contains two major components: hypothesis
tests for prioritizing effects of various uncertainties and Bayesian analysis for further quantifying
their effects. The approach is implemented in a spreadsheet environment and applied to analyze the 1999 Shek
Kip Mei landslide in Hong Kong. Hypothesis tests show that the uncertainty of pore water pressure ratio has
the most significant effect on slope performance and its effect is further quantified by Bayesian analysis.
Bayesian analysis results are validated against independent sensitivity studies. It is shown that probabilistic
failure analysis provides results that are equivalent to those from additional sensitivity studies, but it avoids
additional computational times and efforts for repeated runs of MCS in sensitivity studies.
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摘要 :
This paper presents the results of microstructural analysis of two discrete slip zones at a recent landslide in Hong Kong. The shear modes in terms of kinematics are interpreted based on the microstructural features of the slip zo...
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This paper presents the results of microstructural analysis of two discrete slip zones at a recent landslide in Hong Kong. The shear modes in terms of kinematics are interpreted based on the microstructural features of the slip zones of the landslide. Microstructural characteristics of the two slip zones are distinct due to their differences in physical properties and stress conditions. It is shown that microstructural analysis can be a very effective tool in reconstructing landsliding mechanisms.
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摘要 :
This paper presents the results of microstructural analysis of two discrete slip zones at a recent landslide in Hong Kong. The shear modes in terms of kinematics are interpreted based on the microstructural features of the slip zo...
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This paper presents the results of microstructural analysis of two discrete slip zones at a recent landslide in Hong Kong. The shear modes in terms of kinematics are interpreted based on the microstructural features of the slip zones of the landslide. Microstructural characteristics of the two slip zones are distinct due to their differences in physical properties and stress conditions. It is shown that microstructural analysis can be a very effective tool in reconstructing landsliding mechanisms.
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