Data-Driven Fatigue Failure Probability Updating of OSD by Bayesian Backward Propagation

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
You-Hua Su, Xiao-Wei Ye, Yang Ding, Bin Chen
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Abstract

This study introduces a data-driven approach for updating the fatigue failure probability of the orthotropic steel deck (OSD) using Bayesian backward propagation. The OSD in steel bridges is considered as a parallel system composed of two critical fatigue-prone components, namely, the rib-to-diaphragm and rib-to-deck joints. A probabilistic model for fatigue reliability is established based on the equivalent structural stress method and limit state function. The system-level fatigue reliability model is then constructed, taking into account the correlations between limit states of individual components through Bayesian network forward propagation. The key advantage of the Bayesian network-based framework is its ability to perform backward propagation, allowing for the updating of failure probabilities for critical components when the system-level failure of the OSD is observed. Consequently, the proposed approach enables the identification of vulnerable components through data-driven fatigue failure probability updating. Finally, the approach is applied to a real instrumented steel bridge to determine the time-dependent fatigue failure probability at both the system and component levels over its service life. The results show that the component-level fatigue failure probability model will underestimate the fatigue life in comparison to the system-level model. Meanwhile, the proposed method could identify vulnerable components by quantifying the fatigue failure probability of in-service steel bridges.

Abstract Image

通过贝叶斯后向传播进行数据驱动的 OSD 疲劳失效概率更新
本研究介绍了一种数据驱动方法,利用贝叶斯后向传播更新正交异性钢桥面(OSD)的疲劳破坏概率。钢桥中的 OSD 被视为一个平行系统,由两个关键的易疲劳部件组成,即肋板与横隔梁以及肋板与桥面的连接。基于等效结构应力法和极限状态函数,建立了疲劳可靠性概率模型。然后,通过贝叶斯网络前向传播,考虑到单个部件极限状态之间的相关性,构建了系统级疲劳可靠性模型。基于贝叶斯网络的框架的主要优点是能够进行后向传播,当观测到 OSD 出现系统级故障时,可以更新关键部件的故障概率。因此,所提出的方法能够通过数据驱动的疲劳失效概率更新来识别易损部件。最后,该方法被应用于一座真实的带仪器钢桥,以确定在其使用寿命内系统和组件层面随时间变化的疲劳失效概率。结果表明,与系统级模型相比,部件级疲劳失效概率模型会低估疲劳寿命。同时,所提出的方法可以通过量化在役钢桥的疲劳破坏概率来识别易损部件。
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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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