Structural reliability analysis for parameterized probability box based on efficient global optimization and dimension-reduction method

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning Pub Date : 2025-06-18 DOI:10.1016/j.ijar.2025.109513

Haibo Liu , Wen Lai , Weifeng Luo , Shufeng Zhang , Huichao Xie , Qiong Wang

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引用次数: 0

Abstract

In practical engineering, structural reliability analysis plays an important role in the safe operation of mechanical systems. The parameterized probability-box (p-box) model can effectively capture aleatory and epistemic uncertainties with flexibility and tunability to adapt to different conditions. This paper proposes a structural reliability analysis method for the problem with parameterized p-box based on efficient global optimization (EGO) and the univariate dimension reduction method (UDRM) to efficiently solve the upper and lower bounds of the failure probability of structures. First, the UDRM is used to calculate the origin moments of the performance function. Second, based on the results of the first four moments, the probability density function (PDF) of the performance function is constructed by the maximum entropy method (MEM) to compute the failure probability. Third, the EGO is utilized to obtain the upper and lower bounds of the failure probability of structures. Finally, the effectiveness of the proposed method is demonstrated through five numerical examples.

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基于高效全局优化降维方法的参数化概率盒结构可靠性分析

在实际工程中，结构可靠度分析对机械系统的安全运行起着重要的作用。参数化概率盒（p-box）模型能够有效地捕捉随机不确定性和认知不确定性，具有适应不同条件的灵活性和可调性。针对参数化p-box问题，提出了一种基于有效全局优化（EGO）和单变量降维法（UDRM）的结构可靠性分析方法，以有效求解结构失效概率的上界和下界。首先，使用UDRM计算性能函数的原点矩。其次，基于前4阶矩的结果，利用最大熵法（MEM）构造性能函数的概率密度函数（PDF），计算失效概率；第三，利用EGO求出结构失效概率的上界和下界。最后，通过5个算例验证了所提方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Approximate Reasoning 工程技术-计算机：人工智能

CiteScore

6.90

自引率

12.80%

发文量

170

审稿时长

67 days

期刊介绍： The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.