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Optimal life-cycle mitigation of fatigue failure risk for structural systems

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  • Mendoza, Jorge
  • Bismut, Elizabeth
  • Straub, Daniel
  • Köhler, Jochen

Abstract

Fatigue failure risk can be mitigated both by increasing the design fatigue capacity of the structural components and by conducting more frequent inspection and maintenance actions. The optimal combination of these two types of safety measure is structure dependent. It depends, among others, on the relative cost of the safety measures, the consequences of failure, the level of redundancy, the number of deteriorating components and the statistical dependence among components. In this article, a generic system representation is used to parametrise deteriorating structures according to these system characteristics. Based on this system representation, we investigate patterns of optimal life-cycle fatigue mitigation and provide recommendations for fatigue design. Results show that it can be cost-efficient to achieve system-level safety requirements with high component reliabilities at design and less frequent inspections. Furthermore, we show that the minimum requirements for fatigue design that are typically prescribed in design standards to avoid the need for inspections are not enough unless sufficient redundancy is ensured.

Suggested Citation

  • Mendoza, Jorge & Bismut, Elizabeth & Straub, Daniel & Köhler, Jochen, 2022. "Optimal life-cycle mitigation of fatigue failure risk for structural systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000655
    DOI: 10.1016/j.ress.2022.108390
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    References listed on IDEAS

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    1. Yang, Yiming & Peng, Jianxin & Cai, C.S. & Zhou, Yadong & Wang, Lei & Zhang, Jianren, 2022. "Time-dependent reliability assessment of aging structures considering stochastic resistance degradation process," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Bismut, Elizabeth & Straub, Daniel, 2021. "Optimal adaptive inspection and maintenance planning for deteriorating structural systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Iannacone, Leandro & Sharma, Neetesh & Tabandeh, Armin & Gardoni, Paolo, 2022. "Modeling Time-varying Reliability and Resilience of Deteriorating Infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    4. Yuan, Xian-Xun & Higo, Eishiro & Pandey, Mahesh D., 2021. "Estimation of the value of an inspection and maintenance program: A Bayesian gamma process model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Adumene, Sidum & Khan, Faisal & Adedigba, Sunday & Zendehboudi, Sohrab, 2021. "Offshore system safety and reliability considering microbial influenced multiple failure modes and their interdependencies," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
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    Citations

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    Cited by:

    1. Wang, Haijie & Li, Bo & Lei, Liming & Xuan, Fuzhen, 2024. "Uncertainty-aware fatigue-life prediction of additively manufactured Hastelloy X superalloy using a physics-informed probabilistic neural network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Cheng, Jianda & Cheng, Minghui & Liu, Yan & Wu, Jun & Li, Wei & Frangopol, Dan M., 2024. "Knowledge transfer for adaptive maintenance policy optimization in engineering fleets based on meta-reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    3. Zhang, Ruixing & An, Liqiang & He, Lun & Yang, Xinmeng & Huang, Zenghao, 2024. "Reliability analysis and inverse optimization method for floating wind turbines driven by dual meta-models combining transient-steady responses," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. Lee, Dooyoul & Kwon, Kybeom, 2023. "Dynamic Bayesian network model for comprehensive risk analysis of fatigue-critical structural details," Reliability Engineering and System Safety, Elsevier, vol. 229(C).

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