Journal of Mechanical Science and Technology

, Volume 24, Issue 6, pp 1211–1218 | Cite as

Choosing a suitable sample size in descriptive sampling

Article

Abstract

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.

Keywords

Crude Monte Carlo sampling Descriptive sampling Reliability Sample size 

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Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of MathematicsKorea Air-Force AcademyChungbukKorea
  2. 2.School of Mechanical EngineeringHanyang UniversitySeoulKorea
  3. 3.Department of Mathematics and Research Institute for Natural SciencesHanyang UniversitySeoulKorea

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