• Structural Engineering and Mechanics
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• v.25 no.3
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• pp.347-363
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• 2007

A novel methodology, referred to as Auxiliary Domain Method (ADM), allowing for a very efficient solution of nonlinear reliability problems is presented. The target nonlinear failure domain is first populated by samples generated with the help of a Markov Chain. Based on these samples an auxiliary failure domain (AFD), corresponding to an auxiliary reliability problem, is introduced. The criteria for selecting the AFD are discussed. The emphasis in this paper is on the selection of the auxiliary linear failure domain in the case where the original nonlinear reliability problem involves multiple objectives rather than a single objective. Each reliability objective is assumed to correspond to a particular response quantity not exceeding a corresponding threshold. Once the AFD has been specified the method proceeds with a modified subset simulation procedure where the first step involves the direct simulation of samples in the AFD, rather than standard Monte Carlo simulation as required in standard subset simulation. While the method is applicable to general nonlinear reliability problems herein the focus is on the calculation of the probability of failure of nonlinear dynamical systems subjected to Gaussian random excitations. The method is demonstrated through such a numerical example involving two reliability objectives and a very large number of random variables. It is found that ADM is very efficient and offers drastic improvements over standard subset simulation, especially when one deals with low probability failure events.

• Geomechanics and Engineering
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• v.19 no.3
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• pp.217-227
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• 2019

This study focuses on an improved prediction model to determine the limiting grouting pressure of compaction grouting considering the ground surface upheaval, which is caused by the three-dimensional conical shearing failure. The 2D-dimensional failure curve in Zou and Xia (2016) was improved to a three-dimensional conical shearing failure for compaction grouting through coordinate rotation. The process of compaction grouting was considered as the cavity expansion in infinite Mohr-Coulomb (M-C) soil mass. The prediction model of limiting grouting pressure of compaction grouting was proposed with limit equilibrium principle, which was validated by comparing the results in El-Kelesh et al. (2001) and numerical method. Furthermore, using the proposed prediction model, the vertical and horizontal grouting tube techniques were adopted to deal with the subgrade settlement in Shao-huai highway at Hunan Provence of China. The engineering applicability and effectiveness of the proposed model were verified by the field test. The research on the prediction model for the limiting grouting pressure of compaction grouting provides practical example to the rapid treatment technology of subgrade settlement.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.03a
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• pp.315-322
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• 1999

The probabilistic keyblock concept which was based upon block theory was applied to the example site by using the observed block moulds data. The more was the block failure likelihood (P(B)) which was defined by the product of the joint combination probability, the shape parameter and the instability parameter, the more were the frequencies of failures observed. If we can acquire these data during a tunnel construction stage, they will be used as a very useful data to construct another tunnel in the neighborhood. Furthermore, a sedimentary rock may have larger P(B) values than a crystalline rock, and for the given P(B) value, the percent block moulds are larger in the former than latter.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1169-1175
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• 2009

In this paper, the inferences of data obtained from periodic inspection and type I censoring for the step-stress accelerated life test are studied. The exponential distribution with a failure rate function that a log-linear function of stress and the tampered failure rate model are considered. The maximum likelihood estimators of the model parameters are estimated and also the optimal stress change time which minimize the asymptotic variance of maximum likelihood estimators of parameters is determined. A numerical example will be given to illustrate the proposed inferential procedures and the sensitivity of the asymptotic variance of the estimated mean by the guessed parameters is investigated.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1375-1386
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• 2006

In life testing, the lifetimes of test units under the usual conditions are so long that life testing at usual conditions is impractical. Testing units are subjected to conditions of high stress to yield informations quickly. In this paper, the inferences of parameters on the three step-stress accelerated life testing are studied. The two-parameter exponential distribution with a failure rate function that a log-quadratic function of stress and the tempered failure rate model are considered. We obtain the maximum likelihood estimators of the model parameters and their confidence regions. A numerical example will be given to illustrate the proposed inferential procedures.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.344-350
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• 2001

Piping is a phenomenon where seeping water progressively erodes or washes away soil particles, leaving large voids (Pipes led to the development of channels) in the soil. Piping failure caused by heave can be expected to occur on the downstream side of a hydraulic structure such as fill dams when the uplift forces of seepage exceed the downward forces due to the submerged weight of the soil. The way to prevent erosion and piping and to reduce damaging uplift pressures is to use a protective filter or to construct cutoff wall/imperious blanket. Therefore, all the hydraulic structures faced/with soil materials should be taken the safety against piping into consideration.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.505-514
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• 2000

A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced mixture failure model of Rayleigh and Erlang(2) pattern. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Gibbs steps are proposed to perform the Bayesian inference of such models. For model determination, we explored sum of relative error criterion that selects the best model. A numerical example with simulated data set is given.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1992.04a
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• pp.51-57
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• 1992

The primary objective of this study is the development of second moment methods for the efficient reliability evaluations of structural systems. Two methods are presented. One is the improved first order reliability method (IFORM), and the other is the modified probabilistic network evaluation technique (MPNET). For the purpose of verifying the proposed methods, example analyses are carried out on several cases with two failure modes, a plane frame structure involving three failure modes and simplified parallel member models for fatigue reliability evaluations of offshore structures. Numerical results indicate that the effectiveness of the proposed methods over the conventional ones (i.e., the FORM and the PNET) increases very significantly as the number of failure modes of the system increases.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.553-560
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• 2007

In this paper, we consider two components system which lifetimes have Freund's bivariate exponential model with equal failure rates. We propose Bayesian multiple comparisons procedure for the failure rates of I Freund's bivariate exponential populations based on Dirichlet process priors(DPP). The family of DPP is applied in the form of baseline prior and likelihood combination to provide the comparisons. Computation of the posterior probabilities of all possible hypotheses are carried out through Markov Chain Monte Carlo(MCMC) method, namely, Gibbs sampling, due to the intractability of analytic evaluation. The whole process of multiple comparisons problem for the failure rates of bivariate exponential populations is illustrated through a numerical example.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.765-775
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• 2010

For the accelerated failure time (AFT) model a lot of effort has been devoted to develop effective estimation methods. AFT model assumes a linear relationship between the logarithm of event time and covariates. In this paper we propose a semiparametric support vector machine to consider situations where the functional form of the effect of one or more covariates is unknown. The proposed estimating equation can be computed by a quadratic programming and a linear equation. We study the effect of several covariates on a censored response variable with an unknown probability distribution. We also provide a generalized approximate cross-validation method for choosing the hyper-parameters which affect the performance of the proposed approach. The proposed method is evaluated through simulations using the artificial example.