论文速递 | 结构在疲劳下的生存概率: 基于数据的方法

文摘   2024-09-25 19:00   德国  
Survival probability of structures under fatigue: A data-based approach

结构在疲劳下的生存概率: 基于数据的方法

引用格式 | Cited by
Cartiaux FB, Legoll F, Libal A, Reygner J, 2024. Survival probability of structures under fatigue: A data-based approach. Probabilistic Engineering Mechanics, 77: 103657.
DOI: 10.1016/j.probengmech.2024.103657
摘要 | Abstract
本文讨论了变幅循环载下结构疲劳寿命的概率特性。我们基于最近引入的 Miner 累积损伤准则形式化方法,来估计试验数据下工业结构的生存概率。研究考虑了结构初始状态和加载周期幅值的随机性。结果表明,可通过确定性等效损伤的概念来获得加载周期的变异性,从而提供了一种计算效的结构疲劳寿命评估方法。此外文章强调,Miner 规则与最弱链原则的结合方法通常会系统性高估结构疲劳寿命。在案例研究中高达两倍以上。我们随后引入概率框架来修正这一过高估计。
关键词: 疲劳寿命, 概率建模, Miner 累积损伤规则, 结构健康监测, 基于数据的方法
This article addresses the probabilistic nature of fatigue life in structures subjected to cyclic loading with variable amplitude. Drawing on the formalization of Miner’s cumulative damage rule that we introduced in the recent article (Cartiaux et al., 2023), we apply our methodology to estimate the survival probability of an industrial structure using experimental data. The study considers both the randomness in the initial state of the structure and in the amplitude of loading cycles. The results indicate that the variability of loading cycles can be captured through the concept of deterministic equivalent damage, providing a computationally efficient method for assessing the fatigue life of the structure. Furthermore, the article highlights that the usual combination of Miner’s rule and of the weakest link principle systematically overestimates the structure’s fatigue life. On the case study that we consider, this overestimation reaches a multiplicative factor of more than two. We then describe how the probabilistic framework that we have introduced offers a remedy to this overestimation.
KeywordsFatigue life; Probabilistic modeling; Miner’s cumulative damage rule; Structural health monitoring; Data-based approach.

图 1: 工业测试案例的全流程图

Fig. 1. Flow chart of the whole procedure on the industrial test case

图 2: 起重机上传感器的位置

Fig. 2. Position of the sensors on the crane

图 3: 每一传感器采用的 SN 曲线

Fig. 3. S–N curve used for each sensor

图 4: 每一监测区与结构生存概率随时间变化的函数

Fig. 4. Survival probability of each monitored zone (in color) and of the structure (in black), as a function of time (for C_S = 1 and Δσ_C = 36 MPa)

作者信息 | Authors

François-Baptiste Cartiaux

法国 OSMOS 集团 (OSMOS Group)

Frédéric Legoll通讯作者 (Corresp.)
法国古斯塔夫埃菲尔大学 (Univ Gustave Eiffel路桥学院

Email: frederic.legoll@enpc.fr

Alex Libal

法国古斯塔夫埃菲尔大学 (Univ Gustave Eiffel路桥学院

Julien Reygner
法国巴黎高等路桥学院 (École des Ponts应用数学研究中心



律梦泽 M.Z. Lyu | 编辑 (Ed) 

P.D. Spanos | 审校 (Rev)

陈建兵 J.B. Chen | 审校 (Rev)

彭勇波 Y.B. Peng | 审校 (Rev)

Probab Eng Mech
国际学术期刊 Probabilistic Engineering Mechanics 创立于 1985 年,SCI 收录,JCR Q1,现任主编是美国工程院院士、中国科学院外籍院士、莱斯大学 Pol D. Spanos 教授。
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