Volume 17 Issue 1
Mar.  2022
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LI X J, QIN B, XIAO Y F, et al. An improved random forest-Monte Carlo method and application for structural reliability analysis of A-type independent liquid tank support structure[J]. Chinese Journal of Ship Research, 2022, 17(1): 147–153, 165 doi: 10.19693/j.issn.1673-3185.02181
Citation: LI X J, QIN B, XIAO Y F, et al. An improved random forest-Monte Carlo method and application for structural reliability analysis of A-type independent liquid tank support structure[J]. Chinese Journal of Ship Research, 2022, 17(1): 147–153, 165 doi: 10.19693/j.issn.1673-3185.02181

An improved random forest-Monte Carlo method and application for structural reliability analysis of A-type independent liquid tank support structure

doi: 10.19693/j.issn.1673-3185.02181
  • Received Date: 2020-11-13
  • Rev Recd Date: 2021-01-15
  • Available Online: 2022-01-28
  • Publish Date: 2022-03-02
    © 2022 The Authors. Published by Editorial Office of Chinese Journal of Ship Research. Creative Commons License
    This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  •   Objectives  In response to the increasing depth of research and design on liquefied natural gas (LNG) ship structures, higher requirements are put forward for a reliability analysis method that can quickly and accurately evaluate uncertain factors. This paper proposes a method based on an improved random forest-Monte Carlo method (RF-MC) to solve the calculation of the failure probability of A-type independent liquid tank support structures.  Methods  First, the MC method is used to generate a sample set according to the probability distribution of uncertain factors, then take the local outlier factor (LOF) as the criterion for filtering out sample points near the failure surface. After selecting the sample points, they are calculated using finite element software and added to the training set to train the random forest (RF) model. The generation, filtering and training process is repeated until the approximate model meets the accuracy requirements. Finally, the approximate model is used to determine whether the sample points are invalid, then combined with the MC method to calculate the failure probability of the structure.  Results  Considering the accuracy, complexity and efficiency of the algorithm, and combined with Cases 1 and 2, it is found that the improved RF-MC method has better advantages than MC or biased probability (BP)-MC in analyzing reliability problems. The results of Case 3 show applicability of the method in reliability analysis of an A-type independent liquid tank support structure.  Conclusions  This study provides a feasible technical solution for future optimization design of liquefied gas carriers.
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