• The Journal of Engineering Geology
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• v.20 no.4
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• pp.437-447
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• 2010

A probabilistic analysis of slope stability is an appropriate solution in dealing with uncertainty in problems related to engineering geology. In this study, a Monte Carlo simulation was performed to evaluate the performance function that is Barton's equation. A large number of randomly generated values were obtained for random variables, and the performance function was calculated repeatedly using randomly generated values. A previous study provided information of slope geometry and the random characteristics of random variables such as JRC and JCS. The present approach was adopted to analyze two failed slopes. The probabilities of failure were evaluated for each slope, and sensitivity analysis was performed to assess the influence of each random variable on the probability of failure. The analysis results were then compared with the results of a deterministic analysis, indicating that the probabilistic analysis yielded reliable results.

• Journal of the Korean Geotechnical Society
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• v.23 no.4
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• pp.125-132
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• 2007

The oedometer, radial CRS and Rowe cell tests, composite discharge capacity tests and smear effect tests are carried out to estimate the parameters for the reliability-based design of vertical drain method. Also the sensitivity analysis, the probabilistic and deterministic solutions of radial consolidation theory are presented. The result of probabilistic analysis was compared to that of deterministic analysis using the tested and estimated parameters. The results indicated that the drain spacing in the deterministic method is larger than that in the probabilistic method because the former does not consider the uncertainties in the geotechnical property. The divergence of two methods is dependent on the probability of achieving target degree of consolidation by a given time and the coefficient of variation (COV) of the coefficient of horizontal consolidation ($c_h$).

• Geotechnical Engineering
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• v.5 no.2
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• pp.33-44
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• 1989

In this paper, uncertainties in dynamic soil structure interaction (SSI) of nuclear poi.or plants subjected to seismic loading are studied considering the random characteristics of soils surround- ing the structure. Firstly sensitivity analysis is performed to study the effect of uncertain dynamic soil properties on the response of the structure. Secondly, to take into account the non-neterministic characteristics in analysis caused by random characteristics of the soil properties, Perturbation method and Rosenblueth's Two point estimates were used for this studu. The procedure is based on the comptex response method which is constituted by a combined usage of conventional finite elements for the near field and infinite elements for the far field. Results of the sensitivity analysis show that dynamic soil properties greatly affect the response of the sol.uc- lure. Results of the probabilistic analysis show that the Two-point estimate method produces good agreements with the Perturbation method.

• Journal of the Society of Naval Architects of Korea
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• v.31 no.1
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• pp.142-154
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• 1994

In this paper, stochastic imperfection sensitivity analyses of stiffened cylindrical shells under static load are presented. Multimode formulation is performed for the buckling load calculation based on the Donnell's theory and Galerkin approximation. Random imperfection field theory and response surface method are combined with deterministic bucking analysis scheme to perform stochastic imperfection sensitivity analyses of stiffened cylindrical shells considering random geometric imperfection. From the characteristics of probabilistic bucking load, the relation between reliability index and safety parameter can be obtained in addition to the relation between load and reliability index. Those results can be used to determine the range of required safety parameter and acceptable imperfection.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.4
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• pp.461-466
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• 2013

Advanced fiber-reinforced laminated composites are widely used in various fields of engineering to reduce weight. The material property of each ply is well known; specifically, it is known that ply is less reliable than metallic materials and very sensitive to the loading direction. Therefore, it is important to consider this uncertainty in the design of laminated composites. In this study, reliability analysis is conducted using COMSOL and MATLAB interactions for a laminated composite plate for the case in which the tip deflection is the design requirement and the material property is a random variable. Furthermore, the efficiency and accuracy of the approximation method is identified, and a probabilistic sensitivity analysis is conducted. As a result, we can prove the applicability of the advanced design method for the stabilizer of an underwater vehicle.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.4
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• pp.138-146
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• 2007

Geotechnical performance at the soft ground is strongly dependent on the properties of the soil beneath and adjacent to the structure of interest. These soil properties can be described using deterministic and/or probabilistic models. Deterministic models typically use a single discrete descriptor for the parameter of interest. Probabilistic models describe parameters by using discrete statistical descriptors or probability distribution density functions. The consolidation process depends on several uncertain parameters including the coefficients of consolidation and coefficients of permeability in vertical and horizontal directions. The implication of this uncertain parameter in the design of prefabricated vertical drains for soil improvement is discussed. A sensitivity analysis of the degree of consolidation and calculation of settlements to these uncertain parameters is presented for clayey deposits.

• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.2
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• pp.131-137
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• 2018

This paper analyzes the probability of failure for the equivalence ratio error. The control error of the equivalence ratio is affected by the aleatory and epistemic uncertainties. In general, reliability analysis techniques are easily incorporated to handle the aleatory uncertainty. However, the epistemic uncertainty requires a new approach, as it does not provide an uncertainty distribution. The Bayesian inference incorporates the reliability analysis results to handle both uncertainties. The result gives a distribution of failure probability, whose equivalence ratio does not meet the requirement. This technique can be useful in the analysis of most engineering systems, where the aleatory and epistemic uncertainties exist simultaneously.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.2
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• pp.163-168
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• 2011

Robust optimization or reliability-based design optimization are some of the methodologies that are employed to take into account the uncertainties of a system at the design stage. For applying such methodologies to solve industrial problems, accurate and efficient methods for estimating statistical moments and failure probability are required, and further, the results of sensitivity analysis, which is needed for searching direction during the optimization process, should also be accurate. The aim of this study is to employ the function approximation moment method into the sensitivity analysis formulation, which is expressed as an integral form, to verify the accuracy of the sensitivity results, and to solve a typical problem of reliability-based design optimization. These results are compared with those of other moment methods, and the feasibility of the function approximation moment method is verified. The sensitivity analysis formula with integral form is the efficient formulation for evaluating sensitivity because any additional function calculation is not needed provided the failure probability or statistical moments are calculated.

• Journal of the Korean Geotechnical Society
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• v.18 no.4
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• pp.119-126
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• 2002

A reliability analysis is performed to investigate the influence of the uncertainty from few in-situ samples and inherent heterogeneity of the ground on the probability of failure for a rock cut slope. The results are compared with those of deterministic slope stability analysis. The random variables used are unit weight of the rock, the angle of potential slope of failure, and cohesion and internal friction angle of joints. It was found that the rock slope in which the factor of safety satisfied the minimum safety factor in the deterministic analysis has high probability of failure in the reliability analysis when the weak geological strata are involved in the cut slope. The probability of failure of rock slope is most sensitive to the mean and standard deviation of cohesion in rock joint among the random soil parameters included in the reliability analysis. Sensitivities of the mean values are larger than those of standard deviations, which means that accurate estimation of the mean for the in-situ geotechnical properties is important.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.11
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• pp.1691-1696
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• 2010

The efforts of reflecting the system's uncertainties in design step have been made and robust optimization or reliabilitybased design optimization are examples of the most famous methodologies. The statistical moments of a performance function and the constraints corresponding to probability conditions are involved in the formulation of these methodologies. Therefore, it is essential to effectively and accurately calculate them. The sensitivities of these methodologies have to be determined when nonlinear programming is utilized during the optimization process. The sensitivity of statistical moments and probability constraints is expressed in the integral form and limited to the normal random variable; we aim to expand the sensitivity formulation to nonnormal variables. Additional functional calculation will not be required when statistical moments and failure or satisfaction probabilities are already obtained at a design point. On the other hand, the accuracy of the sensitivity results could be worse than that of the moments because the target function is expressed as a product of the performance function and the explicit functions derived from probability density functions.