Descriptive sampling in structural safety

Žiha, Kalman

doi:10.1016/0167-4730(94)00038-r

Cited by 33 publications

(17 citation statements)

References 6 publications

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“…More details can be found in literature [39,40]. This sampling technique is one of the variance reduction techniques, allowing to reduce the variance of the statistical estimates derived from the MCS data.…”

Section: ( ) P ⋅mentioning

confidence: 99%

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Crashworthiness design of foam-filled bitubal structures with uncertainty

Fang

Gao

Sun

et al. 2014

International Journal of Non-Linear Mechanics

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Section: ( ) P ⋅mentioning

confidence: 99%

“…A simple use of random sampling usually requires much more than a desirable number of sample points and is even impractical for complex and time-consuming analyses. To achieve high accuracy of the statistical description of the system behavior, a descriptive sampling method [39,40] is used in this study.…”

Section: Monte Carlo Simulation and Descriptive Samplingmentioning

confidence: 99%

Crashworthiness design of foam-filled bitubal structures with uncertainty

Fang

Gao

Sun

et al. 2014

International Journal of Non-Linear Mechanics

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“…(4). According to Ziha [19], the minimal sample size is obtained by assuming 1 i = in Eq. (4), but this assumption is not valid because an element of the descriptive set cannot be in the failure region.…”

Section: Determination Of Input Sample Sizementioning

confidence: 99%

“…The estimate obtained by DS is unbiased and has a lower variance than that obtained by the classical method of CMCS [17,18]. In Ziha [19], DS was applied to IS for the reduction of variance in the probability of failure. It has also been used in experimental design [20,21] and risk analysis simulation [22,23].…”

Section: Introductionmentioning

confidence: 99%

Choosing a suitable sample size in descriptive sampling

2010

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Descriptive sampling (DS) is an alternative to crude Monte Carlo sampling (CMCS) in finding solutions to structural reliability problems. It is known to be an effective sampling method in approximating the distribution of a random variable because it uses the deterministic selection of sample values and their random permutation,. However, because this method is difficult to apply to complex simulations, the sample size is occasionally determined without thorough consideration. Input sample variability may cause the sample size to change between runs, leading to poor simulation results. This paper proposes a numerical method for choosing a suitable sample size for use in DS. Using this method, one can estimate a more accurate probability of failure in a reliability problem while running a minimal number of simulations. The method is then applied to several examples and compared with CMCS and conventional DS to validate its usefulness and efficiency.

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“…But, the MC method demonstrates a poor computational efficiency in evaluating problems of small failure probability or problems that require a large number of costly finite element (FE) analyses in each sampling cycle. To address this problem, researchers have developed numerous variance reduction techniques including stratified sampling [2], Latin hypercube sampling [3,4], importance sampling [5,6] and directional sampling [7,8]. Recently, some novel techniques such as subset simulation [9] and line sampling simulation [10] were proposed to treat high-dimensional reliability problems.…”

Section: Introductionmentioning

confidence: 99%