Journal of the Korean Society of Safety (한국안전학회지)

The Korean Society of Safety (한국안전학회)
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Reliability Analysis of Stowage System of Container Crane using Subset Simulation with Markov Chain Monte Carlo Sampling

마르코프 연쇄 몬테 카를로 샘플링과 부분집합 시뮬레이션을 사용한 컨테이너 크레인 계류 시스템의 신뢰성 해석

  • Park, Wonsuk (Department of Civil Engineering, Mokpo National University) ;
  • Ok, Seung-Yong (Department of Civil, Safety & Environmental Engineering, Hankyong National University)
  • 박원석 (목포대학교 토목공학과) ;
  • 옥승용 (한경대학교 토목안전환경공학과)
  • Received : 2017.04.10
  • Accepted : 2017.05.02
  • Published : 2017.06.30
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Abstract

This paper presents an efficient finite analysis model and a simulation-based reliability analysis method for stowage device system failure of a container crane with respect to lateral load. A quasi-static analysis model is introduced to simulate the nonlinear resistance characteristics and failure of tie-down and stowage pin, which are the main structural stowage devices of a crane. As a reliability analysis method, a subset simulation method is applied considering the uncertainties of later load and mechanical characteristic parameters of stowage devices. An efficient Markov chain Monte Carlo (MCMC) method is applied to sample random variables. Analysis result shows that the proposed model is able to estimate the probability of failure of crane system effectively which cannot be calculated practically by crude Monte Carlo simulation method.

Keywords

References

  1. O. Ditlevsen, H. O. Madsen, Structural Reliability Method, Wiley, 1996.
  2. S.-K. Au and J. L. Beck, "Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation", Probabilistic Engineering Mechanics, Vol.16, pp. 263-277, 2001. https://doi.org/10.1016/S0266-8920(01)00019-4
  3. I. Papaionaanou, W. Betz, K. Zwirglmaier and D. Straub, "MCMC algorithms for Subset Simulation", Probabilistic Engineering Mechanics, Vol. 41, pp. 89-103, 2015. https://doi.org/10.1016/j.probengmech.2015.06.006
  4. K. M. Zuev, J. L. Beck, S. K. Au and L. S. Katafygiotis, "Bayesian Post-Processor and Other Enhancements of Subset Simulation for Estimating Failure Probabilities in High Dimensions", Computers & Structures, Vol. 92-93, pp. 283-296, 2012. https://doi.org/10.1016/j.compstruc.2011.10.017
  5. L. Tierney, "Markov Chains for Exploring Posterior Distribution", the Annals of Statistics, pp. 1701-1728, 1994.
  6. J. L. Beck and S. K. Au, "Bayesian Updating of Structural Models and Reliability using Markov Chain Monte Carlo Simulation", Journal of Engineering Mechanics, Vol. 128, No. 4, pp. 380-391, 2002. https://doi.org/10.1061/(ASCE)0733-9399(2002)128:4(380)
  7. G. Fishman, Monte Carlo: Concepts, Algorithms, and Applications. Springer Science & Business Media, 2013.
  8. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller and E. Teller, "Equation of State Calculations by Fast Computing Machines", The Journal of Chemical Physics, Vol. 21, No. 6, pp.1087-1092, 1953. https://doi.org/10.1063/1.1699114