基于新型混合方法的随机结构小故障概率分析

IF 3 3区 工程技术 Q2 ENGINEERING, MECHANICAL Probabilistic Engineering Mechanics Pub Date : 2024-03-19 DOI:10.1016/j.probengmech.2024.103611
Huan Huang , Huiying Wang , Yingxiong Li , Gaoyang Li , Hengbin Zheng
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引用次数: 0

摘要

本文采用两种代用模型和子集模拟法,结合并行计算,研究了随机结构的小失效概率问题。为了实现较高的计算效率,首先以基于显式时域法的形式导出了随机结构动态响应的显式表达式。然后,通过蒙特卡罗仿真方法,利用显式表达有效地进行随机结构的小失效概率分析。为避免重复计算随机结构显式表达式的系数矩阵或向量,引入了两类代用模型,如反向传播神经网络模型和克里金模型,以获得随机结构每个参数样本的系数矩阵或向量。通过使用子集模拟法生成遵循 Metropolis-Hastings 规则的条件样本,可进一步降低计算成本。此外,由于代用模型在每个时间瞬间的独立性和动态分析在每个样本的独立性,并行计算被嵌入到所提出的方法中,这可以充分发挥所提出方法的特点,进一步提高动态可靠性分析的计算效率。本文给出了数值示例来说明所提出的混合方法的有效性。
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Small failure probability analysis of stochastic structures based on a new hybrid approach

The small failure probability problem of stochastic structures is investigated by using two types of surrogate models and the subset simulation method in conjunction with parallel computation. To achieve high computational efficiency, the explicit expression of dynamic responses of stochastic structures is first derived in the form based on the explicit time-domain method. Then, the small failure probability analysis of stochastic structures is efficiently carried out through the Monte Carlo simulation method utilizing explicit expressions. To avoid the repeated calculation for the coefficient matrices or vectors of the explicit expression of stochastic structures, two types of surrogate models, e.g., the backpropagation neural network model and the Kriging model, are introduced to obtain these matrices or vectors for each parameter sample of the stochastic structures. The computational cost is further reduced by using the subset simulation method to generate conditional samples which follow the rule of Metropolis-Hastings. Furthermore, in virtue of the independence of the surrogate models for each time instant and the independence of dynamic analysis for each sample, parallel computation is embedded in the proposed approach, which can fully exploit the characteristics of the proposed approach and further improve the computational efficiency of dynamic reliability analysis. Numerical examples are given to illustrate the validity of the proposed hybrid approach.

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来源期刊
Probabilistic Engineering Mechanics
Probabilistic Engineering Mechanics 工程技术-工程:机械
CiteScore
3.80
自引率
15.40%
发文量
98
审稿时长
13.5 months
期刊介绍: This journal provides a forum for scholarly work dealing primarily with probabilistic and statistical approaches to contemporary solid/structural and fluid mechanics problems encountered in diverse technical disciplines such as aerospace, civil, marine, mechanical, and nuclear engineering. The journal aims to maintain a healthy balance between general solution techniques and problem-specific results, encouraging a fruitful exchange of ideas among disparate engineering specialities.
期刊最新文献
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