• Structural Engineering and Mechanics
• /
• v.75 no.6
• /
• pp.771-784
• /
• 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.

• Geomechanics and Engineering
• /
• v.29 no.5
• /
• pp.487-496
• /
• 2022

Most of the pile's vertical static load tests in construction sites are the proof load tests, which is difficult to accurately estimate the ultimate bearing capacity and analyze the reliability of piles. Therefore, a reliability analysis method based on the proof load-settlement (Q-s) data is proposed in this study. In this proposed method, a simple ultimate limit state function based on the hyperbolic model is established, where the random variables of reliability analysis include the model factor of the ultimate bearing capacity and the fitting parameters of the hyperbolic model. The model factor M = R_uR / R_uP is calculated based on the available destructive Q-s data, where the real value of the ultimate bearing capacity (R_uR) is obtained by the complete destructive Q-s data; the predicted value of the ultimate bearing capacity (R_uP) is obtained by the proof Q-s data, a part of the available destructive Q-s data, that before the predetermined load determined by the pile test report. The results demonstrate that the proposed method can easy and effectively perform the reliability analysis based on the proof Q-s data.

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

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

• Journal of Industrial Technology
• /
• v.28 no.B
• /
• pp.149-156
• /
• 2008

A reliability model of Level II AFDA is developed to analyze the stability of armor units on the sloped coastal structures. Additionally, the partial safety factors of random variables related to armor units can be straightforwardly evaluated by applying the inverse-reliability method in which influence coefficients and uncertainties of random variables, and target probability of failure are combined directly. In particular, a design equation for armor units is derived in terms of the same criteria as deterministic design method in order to apply the reliability-based design method of Level I without some understanding to the reliability analysis. Finally, it is confirmed that several results redesigned by the reliability-based design method of Level I have satisfactorily agreement with results of CEM as well as those of Level II AFDA.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1991.10a
• /
• pp.57-62
• /
• 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
• /
• 2003.04a
• /
• pp.103-110
• /
• 2003

A hybrid structural reliability analysis method that integrates a commercial finite element program and a reliability analysis algorithm is proposed to estimate the safety of real structures in this paper. Since finite element method (FEM) is most commonly and widely used in the analysis and design practice of real structures, it appears to be necessary to use general FEM program in the structural reliability analysis. In this case, simple conventional reliability methods cannot be used because the limit state function can only be expressed in an algorithmic form. The response surface method(RSM)-based reliability algorithm with the first-order reliability method (FORM) found to be ideal in this respect and is used in this paper. The intention of use of RSM is to develop, albeit approximately, an explicit expression of the limit state function for real structures. The applicability of the proposed method to real structures is examined with help of the example in consideration of a concrete dam. Both the strength and serviceability limit states are considered in this example.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.22 no.4
• /
• pp.299-306
• /
• 2009

Design optimization is a method to find optimum point which minimizes the objective function while satisfying design constraints. The conventional optimization does not consider the uncertainty originated from modeling or manufacturing process, so optimum point often locates on the boundaries of constraints. Reliability based design optimization includes optimization technique and reliability analysis that calculates the reliability of the system. Reliability analysis can be classified into simulation method, fast probability integration method, and moment-based reliability method. In most generally used MPP based reliability analysis, which is one of fast probability integration method, if many MPP points exist, cost and numerical error can increase in the process of transforming constraints into standard normal distribution space. In this paper, multiplicative decomposition method is used as a reliability analysis for RBDO, and sensitivity analysis is performed to apply gradient based optimization algorithm. To illustrate whole process of RBDO mathematical and engineering examples are illustrated.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.15 no.4
• /
• pp.435-441
• /
• 2012

This paper provides simulation-based results of effect analysis of sample size and sampling periods on accuracy of reliability estimation methods using multiple comparisons with analysis of variance. Sum of squared errors in estimated reliability measures were evaluated through applying seven estimation methods for one-shot systems to simulated quantal-response data. Analysis of variance was implemented to investigate change in these errors according to variations of sample size and sampling periods for each estimation method, and then the effect analysis on accuracy in reliability estimation was performed using multiple comparisons based on sample size and sampling periods. An efficient way to allocate both sample size and sampling periods for reliability estimation tests of one-shot systems is proposed in this paper from the effect analysis results.

• Journal of the Korean Society of Safety
• /
• v.29 no.4
• /
• pp.116-122
• /
• 2014

This study proposes a system reliability analysis of rack storage facilities subjected to forklift colliding events. The proposed system reliability analysis consists of two steps: the first step is to identify dominant failure modes that most contribute to the failure of the whole rack facilities, and the second step is to evaluate the system failure probability. In the first step, dominant failure modes are identified by using a simulation-based selective searching technique where the contribution of a failure mode to the system failure is roughly estimated based on the distance from the origin in the space of the random variables. In the second step, the multi-scale system reliability method is used to compute the system reliability where the first-order reliability method (FORM) is initially used to evaluate the component failure probability (failure probability of one member), and then the probabilities of the identified failure modes and their statistical dependence are evaluated, which is called as the lower-scale reliability analysis. Since the system failure probability is comprised of the probabilities of the failure modes, a higher-scale reliability analysis is performed again based on the results of the lower-scale analyses, and the system failure probability is finally evaluated. The illustrative example demonstrates the results of the system reliability analysis of the rack storage facilities subjected to forklift impact loadings. The numerical efficiency and accuracy of the approach are compared with the Monte Carlo simulations. The results show that the proposed two-step approach is able to provide accurate reliability assessment as well as significant saving of computational time. The results of the identified failure modes additionally let us know the most-critical members and their failure sequence under the complicated configuration of the member connections.