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A probabilistic computational framework for bridge network optimal maintenance scheduling

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  • Bocchini, Paolo
  • Frangopol, Dan M.

Abstract

This paper presents a probabilistic computational framework for the Pareto optimization of the preventive maintenance applications to bridges of a highway transportation network. The bridge characteristics are represented by their uncertain reliability index profiles. The in/out of service states of the bridges are simulated taking into account their correlation structure. Multi-objective Genetic Algorithms have been chosen as numerical tool for the solution of the optimization problem. The design variables of the optimization are the preventive maintenance schedules of all the bridges of the network. The two conflicting objectives are the minimization of the total present maintenance cost and the maximization of the network performance indicator. The final result is the Pareto front of optimal solutions among which the managers should chose, depending on engineering and economical factors. A numerical example illustrates the application of the proposed approach.

Suggested Citation

  • Bocchini, Paolo & Frangopol, Dan M., 2011. "A probabilistic computational framework for bridge network optimal maintenance scheduling," Reliability Engineering and System Safety, Elsevier, vol. 96(2), pages 332-349.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:2:p:332-349
    DOI: 10.1016/j.ress.2010.09.001
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    Citations

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    Cited by:

    1. Petchrompo, Sanyapong & Parlikad, Ajith Kumar, 2019. "A review of asset management literature on multi-asset systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 181-201.
    2. Andriotis, C.P. & Papakonstantinou, K.G., 2021. "Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    3. Okasha, Nader M. & Frangopol, Dan M. & Orcesi, André D., 2012. "Automated finite element updating using strain data for the lifetime reliability assessment of bridges," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 139-150.
    4. Klerk, Wouter Jan & Kanning, Wim & Kok, Matthijs & Wolfert, Rogier, 2021. "Optimal planning of flood defence system reinforcements using a greedy search algorithm," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    5. Tong Wang & Yang Liu & Qiyuan Li & Peng Du & Xiaogong Zheng & Qingfei Gao, 2023. "State-of-the-Art Review of the Resilience of Urban Bridge Networks," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    6. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    7. Calvert, Gareth & Neves, Luis & Andrews, John & Hamer, Matthew, 2020. "Multi-defect modelling of bridge deterioration using truncated inspection records," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    8. 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).
    9. Andriotis, C.P. & Papakonstantinou, K.G., 2019. "Managing engineering systems with large state and action spaces through deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    10. Nogal, M. & Honfi, D., 2019. "Assessment of road traffic resilience assuming stochastic user behaviour," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 72-83.
    11. Fan Zhang & Pingyi Wang & Huaihan Liu & Bin Zhang & Jianle Sun & Jian Li, 2023. "Inland Waterway Infrastructure Maintenance Prediction Model Based on Network-Level Assessment," Sustainability, MDPI, vol. 15(22), pages 1-17, November.

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