A Novel Two-Stage Reliability Analysis Method Combining Improved Cross-Entropy Adaptive Sampling and Relevant Vector Machine

IF 2.7 3区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Xin Fan, Xufeng Yang, Yongshou Liu
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引用次数: 0

Abstract

The computational burden becomes unbearable when reliability analysis involves time-consuming finite element analysis, especially for rare events. Therefore, reducing the number of performance function calls is the only way to improve computing efficiency. This paper proposes a novel reliability analysis method that combines relevant vector machine (RVM) and improved cross-entropy adaptive sampling (iCE). In this method, RVM is employed to approximate the limit state surface and iCE is performed based on the constructed RVM. To guarantee the precision of RVM, the first level samples and the last level samples of iCE are used as candidate samples and the last level samples are regenerated along with the RVM updates. To prevent unnecessary updates of RVM, the proposed method considers the positions of the samples in the current design of experiment. In addition, based on the statistical properties of RVM and iCE, an error-based stopping criterion is proposed. The accuracy and efficiency of the proposed method were validated through four benchmark examples. Finally, the proposed method is applied to engineering problems which are working in extreme environment.

一种结合改进交叉熵自适应采样和相关向量机的两阶段可靠性分析方法
当可靠性分析涉及到耗时的有限元分析时,计算量变得难以承受,特别是对于罕见事件。因此,减少性能函数调用的次数是提高计算效率的唯一途径。提出了一种将相关向量机(RVM)和改进的交叉熵自适应采样(iCE)相结合的可靠性分析方法。在该方法中,利用RVM近似极限状态面,并基于构造的RVM进行iCE。为了保证RVM的精度,使用iCE的第一层样本和最后一层样本作为候选样本,最后一层样本随着RVM的更新而重新生成。为了避免对RVM进行不必要的更新,本文提出的方法考虑了当前实验设计中样本的位置。此外,基于RVM和iCE的统计特性,提出了一种基于误差的停止准则。通过4个基准算例验证了该方法的准确性和有效性。最后,将该方法应用于极端环境下的工程问题。
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来源期刊
CiteScore
5.70
自引率
6.90%
发文量
276
审稿时长
5.3 months
期刊介绍: The International Journal for Numerical Methods in Engineering publishes original papers describing significant, novel developments in numerical methods that are applicable to engineering problems. The Journal is known for welcoming contributions in a wide range of areas in computational engineering, including computational issues in model reduction, uncertainty quantification, verification and validation, inverse analysis and stochastic methods, optimisation, element technology, solution techniques and parallel computing, damage and fracture, mechanics at micro and nano-scales, low-speed fluid dynamics, fluid-structure interaction, electromagnetics, coupled diffusion phenomena, and error estimation and mesh generation. It is emphasized that this is by no means an exhaustive list, and particularly papers on multi-scale, multi-physics or multi-disciplinary problems, and on new, emerging topics are welcome.
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