• KSCE Journal of Civil and Environmental Engineering Research
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• v.3 no.3
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• pp.95-106
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• 1983

The probability of failure is used to analyze the reliability of three dimensional slope failure, instead of conventional factor of safety. The strength parameters are assumed to be normal variated and beta variated. These are interval estimated under the specified confidence level and maximum likelihood estimation. The pseudonormal and beta random variables are generated using the uniform probability transformation method according to central limit theorem and rejection method. By means of a Monte-Carlo Simulation, the probability of failure is defined as; $$P_f$$=M/N N: Total number of trials M: Total number of failures some of the conclusions derived from the case study include; 1. If the strength parameters are assumed to be normal variated, the relationship between safety factor and the probability of failure is fairly consistent, regardless of the procedures of analysis and dimensions of assumed rupture surfaces. 2. However if the strength parameters are beta variated, general relationship between $F_s$ and $P_f$ is hardly found.

• Computational Structural Engineering
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• v.7 no.1
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• pp.109-116
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• 1994

In this paper, various reliability analysis methods for calculating a probability of failure are investigated for their accuracy and efficiency. Crude Monte Carlo method is used as a basis for the comparison of various numerical results. For the sampling methods, Importance Sampling method and Directional Simulation method are considered for overcoming a drawback of Crude Monte Carlo method. For the approximate methods, conventional Rackwitz-Fiessler method. 3-parameter Chen-Lind method, and Rosenblatt transformation method are compared on the basis of First order reliability method. As a Second-order reliability method, Curvature-Fitting paraboloid method, Point-fitting paraboloid method, and Log-likelihood function method are explored in order to verify the accuracy of the reliability calculation results. These methods mentioned above would have some difficulty unless the limit state equation is expressed explicitly in terms of random design variables. Thus, there is a need to develop some general reliability methods for the case where an implicit limit state equation is given. For this purpose, Response surface method is used where the limit state equation is approximated by regression analysis of the response surface outcomes resulted from the structural analysis. From the application of these various reliability methods to three examples, it is found that Directional Simulation method and Response Surface method are very efficient and recommendable for the general reliability analysis problem cases.

• Journal of KIISE:Information Networking
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• v.30 no.1
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• pp.24-29
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• 2003

In this paper, a new method for the mixed image separation is presented using the independent component analysis, the innovation process, and the expectation-maximization. In general, the independent component analysis (ICA) is one of the widely used statistical signal processing schemes, which represents the information from observations as a set of random variables in the from of linear combinations of another statistically independent component variables. In various useful applications, ICA provides a more meaningful representation of the data than the principal component analysis through the transformation of the data to be quasi-orthogonal to each other. which can be utilized in linear projection.. However, it has been known that ICA does not establish good performance in source separation by itself. Thus, in order to overcome this limitation, there have been many techniques that are designed to reinforce the good properties of ICA, which improves the mixed image separation. Unfortunately, the innovation process still needs to be studied since it yields inconsistent innovation process that is attached to the ICA, the expectation and maximization process is added. The results presented in this paper show that the proposed improves the image separation as presented in experiments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.4 no.3
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• pp.75-83
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• 1984

The probability of failure is used to analyze the reliability of three dimensional slope failure, instead of conventional factor of safety. The strength parameters are assumed to be normal variated and beta variated. These are interval estimated under the specified confidence level and maximum likelihood estimation. The pseudonormal and beta random variables are generated using the uniform probability transformation method according to central limit theorem and rejection method. By means of a Monte-Carlo Simulation, the probability of failure is defined as; $P_f=M/N$ N: Total number of trials M: Total number of failures Some of the conclusions derived. from the case study include; 1. Three dimensional factors of safety are generally much higher than 2-D factors of safety. However situations appear to exist where the 3-D factor of safety can be lower than the 2-D factor of safety. 2. The $F_3/F_2$ ratio appears to be quite sensitive to c and ${\phi}$ and to the shape of the 3-D shear surface and the slope but not to be to the unit weight of soil. 3. From the two models (normal, beta) considered for the distribution of the factor of safety, the beta distribution generally provides lager than normal distribution. 4. Results obtained using the beta and normal models are presented in a nomgraph relating slope height and slop angle to probability of failure.