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Optimal adaptive inspection and maintenance planning for deteriorating structural systems

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  • Bismut, Elizabeth
  • Straub, Daniel

Abstract

Optimizing inspection and maintenance (I&M) plans for a large deteriorating structure is a computationally challenging task, in particular if one considers interdependences among its components. This is due to the sheer number of possible decision alternatives over the lifetime of the structure and the uncertainty surrounding the deterioration processes, the structural performance and the outcomes of inspection and maintenance actions. To address this challenge, Luque and Straub (2019) proposed a heuristic approach in which I&M plans for structural systems are defined through a set of simple decision rules. Here, we formalize the optimization of these decision rules and extend the approach to enable adaptive planning. The initially optimal I&M plan is successively adapted throughout the service life, based on past inspection and monitoring results. The proposed methodology uses stochastic deterioration models and accounts for the interdependence among structural components. The heuristic-based adaptive planning is illustrated for a structural frame subjected to fatigue.

Suggested Citation

  • Bismut, Elizabeth & Straub, Daniel, 2021. "Optimal adaptive inspection and maintenance planning for deteriorating structural systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021004063
    DOI: 10.1016/j.ress.2021.107891
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    Cited by:

    1. Morato, P.G. & Andriotis, C.P. & Papakonstantinou, K.G. & Rigo, P., 2023. "Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Zhao, Yunfei & Smidts, Carol, 2022. "Reinforcement learning for adaptive maintenance policy optimization under imperfect knowledge of the system degradation model and partial observability of system states," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    3. Bismut, Elizabeth & Pandey, Mahesh D. & Straub, Daniel, 2022. "Reliability-based inspection and maintenance planning of a nuclear feeder piping system," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    4. Finkelstein, Maxim & Cha, Ji Hwan & Langston, Amy, 2022. "Optimal preventive switching of components in degrading systems," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. Salem, Marwa Belhaj & Fouladirad, Mitra & Deloux, Estelle, 2022. "Variance Gamma process as degradation model for prognosis and imperfect maintenance of centrifugal pumps," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    6. Marko Kinne & Sebastian Thöns, 2023. "Fatigue Reliability Based on Predicted Posterior Stress Ranges Determined from Strain Measurements of Wind Turbine Support Structures," Energies, MDPI, vol. 16(5), pages 1-26, February.
    7. 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).
    8. 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).

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