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Reliability Analysis Based on Optimization Random Forest Model and MCMC

Fan Yang1,2,3,*, Jianwei Ren1,2
1 State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
2 College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
3 State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University, Xi’an, 710049, China
* Corresponding Author: Fan Yang. Email:
(This article belongs to this Special Issue: Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)

Computer Modeling in Engineering & Sciences 2020, 125(2), 801-814. https://doi.org/10.32604/cmes.2020.08889

Received 22 October 2019; Accepted 07 August 2020; Issue published 12 October 2020

Abstract

Based on the rapid simulation of Markov Chain on samples in failure region, a novel method of reliability analysis combining Monte Carlo Markov Chain (MCMC) with random forest algorithm was proposed. Firstly, a series of samples distributing around limit state function are generated by MCMC. Then, the samples were taken as training data to establish the random forest model. Afterwards, Monte Carlo simulation was used to evaluate the failure probability. Finally, examples demonstrate the proposed method possesses higher computational efficiency and accuracy.

Keywords

Markov chain; random forest; Monte Carlo simulation; nonlinear function; random uncertainty

Cite This Article

Yang, F., Ren, J. (2020). Reliability Analysis Based on Optimization Random Forest Model and MCMC. CMES-Computer Modeling in Engineering & Sciences, 125(2), 801–814.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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