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

Xuejian LI; Bin QIN; Yifeng XIAO; Zekun FU

doi:10.19693/j.issn.1673-3185.02181

Chinese Journal of Ship Research, Volume. 17, Issue 1, 147(2022)

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

Xuejian LI, Bin QIN, Yifeng XIAO, and Zekun FU

Author Affiliations

Jiangnan Institute of Technology, Jiangnan Shipyard (Group) Co., Ltd., Shanghai 201913, China

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References(6)

[1] [1] CHOI S K, CANFIELD R A, GRHI R V. Reliabilitybased structural design[M]. London: Springer, 2007.

[5] BREUNIG M M, KRIEGEL H P, NG R T et al. LOF: identifying density-based local outliers[J]. ACM Sigmod Record, 29, 93-104(2000).

[6] BREIMAN L. Random forests[J]. Machine Learning, 45, 5-32(2001).

[7] BREIMAN L. Bagging predictors[J]. Machine Learning, 24, 123-140(1996).

[9] FREUND Y, SCHAPIRE R E. A decision-theoretic generalization of on-line learning and an application to boosting[J]. Journal of Computer and System Sciences, 55, 119-139(1997).

[13] [13] RUBINSTEIN R Y. Simulation the Monte Carlo method[M]. New Yk: John Wiley Sons, Inc. , 1981.

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Xuejian LI, Bin QIN, Yifeng XIAO, Zekun FU. 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

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Paper Information

Category: Ship Structure and Fittings

Received: Nov. 13, 2020

Accepted: --

Published Online: Mar. 24, 2025

The Author Email:

DOI:10.19693/j.issn.1673-3185.02181

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