• Korean Journal of Mathematics
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• 제5권1호
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• pp.55-60
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• 1997

A continuum structure function(CSF) is a non-decreasing mapping from the unit hypercube to the unit interval. The reliability importance of component $i$ in a CSF at system level ${\alpha}$, $R_i({\alpha})$) say, is zero if and only if component $i$ is almost irrelevant to the system at level ${\alpha}$. A condition to check whether a component is almost irrelevant to the system is presented. It is shown that $R^{(m)}_i({\alpha}){\rightarrow}R_i({\alpha})$ uniformly as $m{\rightarrow}{\infty}$ where each $R^{(m)}_i({\alpha})$ is readily calculated.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1999년도 가을 학술발표회 논문집
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• pp.351-358
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• 1999

The structural responses of underground structures are examined in probability by using the elasto-plastic stochastic finite element method in which the spatial distributions of material properties are assumed to be stochastic fields. In addition, the adaptive importance sampling method using the response surface technique is used to improve simulation efficiency. The method is found to provide appropriate information although the nonlinear Limit State involves a large number of basic random variables and the failure probability is small. The probability of plastic local failures around an excavated area is effectively evaluated and the reliability for the limit displacement of the ground is investigated. It is demonstrated that the adaptive importance sampling method can be very efficiently used to evaluate the reliability of a large scale stochastic finite element model, such as the underground structures located in the multi-layered ground.

• 산업경영시스템학회지
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• 제24권63호
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• pp.33-43
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• 2001

To estimate the reliability of a large and complex network with a small variance, we propose two dynamic Monte Carlo sampling methods: the sequential minimal cut set (SMCS) and the sequential minimal path set (SMPS) methods. These methods do not require all minimal cut sets or path sets to be given in advance and do not simulate all arcs at each trial, which can decrease the valiance of network reliability. Based on the proposed methods, we develop the importance sampling estimators, the total hazard (or safety) estimator and the hazard (or safety) importance sampling estimator, and compare the performance of these simulation estimators. It is found that these estimators can significantly reduce the variance of the raw simulation estimator and the usual importance sampling estimator. Especially, the SMCS algorithm is very effective in case that the failure probabilities of arcs are low. On the contrary, the SMPS algorithm is effective in case that the success Probabilities of arcs are low.

• 한국신뢰성학회지:신뢰성응용연구
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• 제3권1호
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• pp.1-12
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• 2003

A continuum structure function(CSF) is a non-decreasing mapping from the unit hypercube to the unit interval. A Barlow-Wu type CSF is a CSF whose behaviour is modeled by its underlying binary structure, which is based on the multistate structure functions suggested by Barlow and Wu(1978). As the measures of importance of a system component for a Barlow-Wu type CSF, the structural and reliability importance of a component at system level ${\alpha}$ (0< ${\alpha}$ <1) are defined and their properties are deduced. Computational results are discussed as well for illustrative purpose.

• Structural Engineering and Mechanics
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• 제5권2호
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• pp.115-126
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• 1997

Importance sampling methods have been developed with the aim of reducing the computational costs inherent in Monte Carlo methods. This study proposes a new algorithm called the adaptive kernel method which combines and modifies some of the concepts from adaptive sampling and the simple kernel method to evaluate the structural reliability of time variant problems. The essence of the resulting algorithm is to select an appropriate starting point from which the importance sampling density can be generated efficiently. Numerical results show that the method is unbiased and substantially increases the efficiency over other methods.

• 산업공학
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• 제10권2호
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• pp.41-50
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• 1997

This paper considers the reliability computation of the network with dependent components and a software system is developed for supporting the reliability analysis and improvement of the system reliability. At first, We propose the common cause failure and load sharing models as the typical models considering the dynamic behaviors of networks with dependent components. Secondly, the algorithm is proposed to obtain the network reliability and reliability importance of component. The software, Delphi, is used to develop the our reliability analysis system.

• Structural Engineering and Mechanics
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• 제84권3호
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• pp.323-335
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• 2022

The main idea of the framework is to seamlessly combine a reasonably accurate and fast surrogate model with the importance sampling strategy. Developing a surrogate model for predicting structures' dynamic responses is challenging because it involves high-dimensional inputs and outputs. For this purpose, a novel surrogate model based on cutting-edge deep learning architectures specialized for capturing temporal relationships within time-series data, namely Long-Short term memory layer and Transformer layer, is designed. After being properly trained, the surrogate model could be utilized in place of the finite element method to evaluate structures' responses without requiring any specialized software. On the other hand, the importance sampling is adopted to reduce the number of calculations required when computing the failure probability by drawing more relevant samples near critical areas. Thanks to the portability of the trained surrogate model, one can integrate the latter with the Importance sampling in a straightforward fashion, forming an efficient framework called TTIS, which represents double advantages: less number of calculations is needed, and the computational time of each calculation is significantly reduced. The proposed approach's applicability and efficiency are demonstrated through three examples with increasing complexity, involving a 1D beam, a 2D frame, and a 3D building structure. The results show that compared to the conventional Monte Carlo simulation, the proposed method can provide highly similar reliability results with a reduction of up to four orders of magnitudes in time complexity.

• 보건의료산업학회지
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• 제13권4호
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• pp.253-263
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• 2019

Objectives: This study evaluated the perception on importance of interprofessional core competencies (PI-ICCP) scale. Methods: Data were collected from 353 college students of health. Content validity was tested using the content validity index for individual items(I-CVI) and for scale(S-CVI). Criterion validity was tested using the professional competencies scale developed by Choi. Reliability was evaluated using Cronbach's coefficient alpha. The goodness-of-fit of the construct validity was determined through exploratory and confirmatory factor analyses. Results: The I-CVI of each item was .8 or higher for all items, and the S-CVI was .98. The reliability of the PI-IPCC was Cronbach's α=.98. The goodness-of-fit indices of the model were χ2=1811.54(p<.001), the comparative fit index (CFI)=.91, and root mean square error of approximation (RMSEA)=.08, which satisfied the criteria. Conclusions: The construct and criterion-related validity of the perception for PI-ICCP scale were a good fit, so the instrument is appropriate for measuring perception on importance of interprofessional core competencies. Further research will be required using this instrument to investigate perception of interprofessional core competencies of health professionals.

• 한국군사과학기술학회지
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• 제15권1호
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• pp.93-100
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• 2012

In this paper, the deterministic optimal design for the tail plane made of composite materials is conducted under the deterministic loading condition and compared with that of the metallic materials. Next, the reliability analysis with five random variables such as loading and material properties of unidirectional prepreg is conducted to examine the probability of failure for the deterministic optimal design results. The MATLAB programing is used for reliability analysis combined with FEA S/W(COMSOL) for structural analysis. The laminated composite is assumed to the equivalent orthotropic material using classical laminated plate theory. The response surface methodology and importance sampling technique are adopted to reduce computational cost with satisfying the accuracy in reliability analysis. As a result, structural weight of composite materials is lighter than that of metals in deterministic optimal design. However, the probability of failure for the deterministic optimal design of the tail plane structures is too high to be neglected. The sensitivity of each variable is also estimated using probabilistic sensitivity analysis to figure out which variables are sensitive to failure. The computational cost is considerably reduced when response surface methodology and importance sampling technique are used. The study of the computationally inexpensive method for reliability-based design optimization will be necessary in further work.

• Structural Engineering and Mechanics
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• 제75권6호
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• pp.771-784
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• 2020

Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive Gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of Gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation. The efficiency, superiority and practicability of AGPC-IS are verified by three examples.