• Structural Engineering and Mechanics
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• v.38 no.1
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• pp.125-140
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• 2011

This study presents an innovative method to estimate the reliability sensitivity based on the low-discrepancy sampling which is a new technique for structural reliability analysis. Two advantages are contributed to the method: one is that, by developing a general importance sampling procedure for reliability sensitivity analysis, the partial derivative of the failure probability with respect to the distribution parameter can be directly obtained with typically insignificant additional computations on the basis of structural reliability analysis; and the other is that, by combining various low-discrepancy sequences with the above importance sampling procedure, the proposed method is far more efficient than that based on the classical Monte Carlo method in estimating reliability sensitivity, especially for problems of small failure probability or problems that require a large number of costly finite element analyses. Examples involving both numerical and structural problems illustrate the application and effectiveness of the method developed, which indicate that the proposed method can provide accurate and computationally efficient estimates of reliability sensitivity.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1991.10a
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• pp.57-62
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• 1991

For the purpose of developing the method for efficiently calculating the design sensitivity and the reliability for the complicated structure such as ship structure, the probabilistic finite element method is introduced to formulate the deterministic design sensitivity analysis method and incorporated with the second moment reliability methods such as MVFOSM, AFOSM and SORM. Also, the probabilistic design sensitivity analysis needed in the reliability-based design is performed. The reliability analysis is carried out for the initial yielding failure, in which the derivative derived in the deterministic desin sensitivity is used. The present PFEM-based reliability method shows good agreement with Monte Carlo method in terms with the variance of response and the associated probability of failure even at the first or first few iteration steps. The probabilistic design sensitivity analysis evaluates explicitly the contribution of each random variable to probability of failure. Further, the reliability index variation can be easily predicted by the variation of the mean and the variance of the random variables.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.101-108
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• 2000

An optimum design algorithm using efficient reanalysis is proposed for reliability-based optimization problems formulated as the minimization of initial cost and expected failure cost with reliability constraints. The reliability-based optimization is high cost to evaluate objective function and constraints needed reliability analysis. Therefore the sensitivity analysis of reliability index for approximated reanalysis is necessary. In this paper, three solution approaches are suggested and tested. The approaches include : (1) sensitivity analysis using finite difference; (2) sensitivity analysis using automatic differentiation (AD); and (3) sensitivity analysis with respect to intermediate variables using AD. Numerical example is optimized to show the reliability and effectiveness of the new algorithm.

• International Journal of Concrete Structures and Materials
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• v.6 no.2
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• pp.101-110
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• 2012

This paper focuses on the finite element (FE) response sensitivity and reliability analyses considering smooth constitutive material models. A reinforced concrete frame is modeled for FE sensitivity analysis followed by direct differentiation method under both static and dynamic load cases. Later, the reliability analysis is performed to predict the seismic behavior of the frame. Displacement sensitivity discontinuities are observed along the pseudo-time axis using non-smooth concrete and reinforcing steel model under quasi-static loading. However, the smooth materials show continuity in response sensitivity at elastic to plastic transition points. The normalized sensitivity results are also used to measure the relative importance of the material parameters on the structural responses. In FE reliability analysis, the influence of smoothness behavior of reinforcing steel is carefully noticed. More efficient and reasonable reliability estimation can be achieved by using smooth material model compare with bilinear material constitutive model.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.780-786
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• 2013

In the reliability-based design optimization of electromagnetic devices, the accurate and efficient reliability assessment method is very essential. The first-order sensitivity-assisted Monte Carlo Simulation is proposed in the former research. In order to improve its accuracy for wide application, in this paper, the second-order sensitivity analysis is presented by using the hybrid direct differentiation-adjoint variable method incorporated with the finite element method. By combining the second-order sensitivity with the Monte Carlo Simulation method, the second-order sensitivity-assisted Monte Carlo Simulation algorithm is proposed to implement reliability calculation. Through application to one superconductor magnetic energy storage system, its accuracy is validated by comparing calculation results with other methods.

• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.331-338
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• 2013

A new reliability calculation method is proposed based on design sensitivity analysis by the finite element method for nonlinear performance constraints in the optimal design of electromagnetic devices. In the proposed method, the reliability of a given design is calculated by using the Monte Carlo simulation (MCS) method after approximating a constraint function to a linear one in the confidence interval with the help of its sensitivity information. The validity and numerical efficiency of the proposed sensitivity-assisted MCS method are investigated by comparing its numerical results with those obtained by using the conventional MCS method and the first-order reliability method for analytic functions and the TEAM Workshop Problem 22.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.581-587
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• 2011

Reliability estimation methods using attribute data are widely used in reliability evaluation of various systems such as nuclear energy plants, food and drug, and space launch vehicles. This paper shows sensitivity analysis and comparison results of reliability estimation methods including a parametric estimation method in open literature with respect to both sample size and sampling points of time. And ways to improve accuracy of each reliability estimation method were proposed from the sensitivity analysis results.

• Journal of Applied Reliability
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• v.16 no.4
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• pp.305-312
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• 2016

Purpose: The purpose of this study is to provide a sensitivity analysis of system reliability for recognizing effectiveness of changing of BD mode failures using reliability growth projection model based on NHPP. Methods: Crow extended reliability projection model (CERPM) is used to analyze the changing of two factors 1) the number of BD mode failures, 2) fix effectiveness factor (FEF) values. Results: The system reliability has increased in accordance with the number of BD mode failures and FEF values have increased. Conclusion: It is necessary to design failure modes and FEF values to supervise the reliability.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.764-769
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• 1988

In this paper the failure possibility and the error possibility are used to represent reliability of a technical component and that of a human operator, respectively. The failure possibility and the error possibility are fuzzy sets on the interval [0,1]. In a man-machine system, reliability of the technical component and that of the human operator are usually affected by many factors, e.g., the environment in which a machine is operated, psychological stress of the human operator, etc. The possibility is derived from not only the failure or the error rate but also estimates of these factors. The fuzzy reasoning plays an important role in the derivation. The reliability analysis is performed by the use of the possibility obtained by the present method. Moreover this paper discusses the sensitivity analysis which evaluates what extent the change of the estimation of each factor has an influence on reliability of a man-machine system. The important factors to be ameliorated are shown through the sensitivity analysis.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.508-518
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• 2008

Reliability and sensitivity analysis of the design parameters for a section of caisson type quaywall which is the most applicable in Korea were performed. It was tried to estimate probabilities of failure for the system of the multiple failure modes and to analyze LCC in the quaywall structure. The reliability analysis was performed by FORM. Also, sensitivity indices were estimated using the reliability indices, which may be used inferring effects of each design parameter on the reliability indices. As a result, the coefficient of friction between caisson and rubble, the moment by self weight and the moment of resistance mostly affected on the reliability indices in the sliding, overturning and foundation failure, respectively. System reliability theorem was applied in order to estimate the probabilities of failure for the system of the multiple failure modes. As the results of estimation of the probabilities of failure for the system, all cases were more conservative than those for the elements, according to both failure mode and load combination applied to series system. It entirely exceeded the target reliability index, but it was consistent with the theorem. According to the optimum LCC with the width of the caisson, the probability of failure exceeded the target probability of failure at then time. Therefore, it was judged to be insufficient to the practical application.