• 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.

• Journal of Industrial Technology
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• v.3
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• pp.53-63
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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; Pf=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 F3/F2 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. In cases that strength parameters are assumed to be normal variated and beta variated, the relationships between safety factor and the probability of failure are fairly consistent, regardless of the shape of the 3-D shear surface and the slope. 4. As the c-value is increased, the probability of failure for the same safety factor is increased and as the ${\phi}-value$ is increased, the probability of failure for the same safety factor is decreased.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.52-61
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• 2013

In wireless sensor networks, the geographical positioning scheme is one of core technologies for sensor applications such as disaster monitoring and environment monitoring. For this reason, studies on range-free positioning schemes have been actively progressing. The density probability scheme based on central limit theorem and normal distribution was proposed to improve the location accuracy in non-uniform sensor network environments. The density probability scheme measures the final positions of unknown nodes by estimating distance through the sensor node communication. However, it has a problem that all of the neighboring nodes have the same 1-hop distance. In this paper, we propose an efficient sensor positioning scheme that overcomes this problem. The proposed scheme performs the second positioning step through the sensing range control after estimating the 1-hop distance of each node in order to minimize the estimation error. Our experimental results show that our proposed scheme improves the accuracy of sensor positioning by about 9% over the density probability scheme and by about 48% over the DV-HOP scheme.

• 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.