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Monte Carlo simulation using support vector machine and kernel density for failure probability estimation

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  • Lee, Seunggyu

Abstract

Monte Carlo simulation requires a large number of sampling points. In a Monte Carlo simulation, the performance function is calculated for all sampling points to determine the failure of the design. If the calculation of the performance function involves large numerical models, a tremendous numerical cost is inevitable. In this study, a support vector machine was applied as a metamodel of the performance function to overcome this drawback. Kernel density and a modified margin of the support vector machine were used for the active learning of the support vector machine. The proportion of the support vector machine's modified margin in the design space was applied as the criterion to end active learning. The proposed method is applied to some numerical examples and examined.

Suggested Citation

  • Lee, Seunggyu, 2021. "Monte Carlo simulation using support vector machine and kernel density for failure probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
  • Handle: RePEc:eee:reensy:v:209:y:2021:i:c:s0951832021000478
    DOI: 10.1016/j.ress.2021.107481
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    1. Echard, B. & Gayton, N. & Lemaire, M. & Relun, N., 2013. "A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 232-240.
    2. Cadini, F. & Avram, D. & Pedroni, N. & Zio, E., 2012. "Subset Simulation of a reliability model for radioactive waste repository performance assessment," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 75-83.
    3. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2020. "A system active learning Kriging method for system reliability-based design optimization with a multiple response model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    4. Cadini, F. & Santos, F. & Zio, E., 2014. "An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 109-117.
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