IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v156y2016icp134-147.html

Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems

Author

Listed:
  • Tien, Iris
  • Der Kiureghian, Armen

Abstract

Novel algorithms are developed to enable the modeling of large, complex infrastructure systems as Bayesian networks (BNs). These include a compression algorithm that significantly reduces the memory storage required to construct the BN model, and an updating algorithm that performs inference on compressed matrices. These algorithms address one of the major obstacles to widespread use of BNs for system reliability assessment, namely the exponentially increasing amount of information that needs to be stored as the number of components in the system increases. The proposed compression and inference algorithms are described and applied to example systems to investigate their performance compared to that of existing algorithms. Orders of magnitude savings in memory storage requirement are demonstrated using the new algorithms, enabling BN modeling and reliability analysis of larger infrastructure systems.

Suggested Citation

  • Tien, Iris & Der Kiureghian, Armen, 2016. "Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 134-147.
    • Handle: RePEc:eee:reensy:v:156:y:2016:i:c:p:134-147
    DOI: 10.1016/j.ress.2016.07.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832016302988
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2016.07.022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Salmeron, Antonio & Cano, Andres & Moral, Serafin, 2000. "Importance sampling in Bayesian networks using probability trees," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 387-413, October.
    2. Der Kiureghian, Armen & Song, Junho, 2008. "Multi-scale reliability analysis and updating of complex systems by use of linear programming," Reliability Engineering and System Safety, Elsevier, vol. 93(2), pages 288-297.
    3. Yeh, Wei-Chang, 2006. "A new algorithm for generating minimal cut sets in k-out-of-n networks," Reliability Engineering and System Safety, Elsevier, vol. 91(1), pages 36-43.
    4. Bensi, Michelle & Kiureghian, Armen Der & Straub, Daniel, 2013. "Efficient Bayesian network modeling of systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 200-213.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ebrahimi, Nader & Shehadeh, Mahmoud, 2015. "Assessing the reliability of components with micro- and nano-structures when they are part a multi-scale system," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 13-20.
    2. Rodríguez, Joanna & Lillo, Rosa E. & Ramírez-Cobo, Pepa, 2015. "Failure modeling of an electrical N-component framework by the non-stationary Markovian arrival process," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 126-133.
    3. Ghafory-Ashtiany, Mohsen & Arghavani, Mahban, 2022. "Physical performance of power grids against earthquakes: from framework to implementation," International Journal of Critical Infrastructure Protection, Elsevier, vol. 39(C).
    4. Roma, Giovanni & Di Maio, Francesco & Zio, Enrico, 2026. "Decision making under uncertainty by trustworthy dynamic bayesian networks for severe accident management in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 267(PA).
    5. Mrinal Kanti Sen & Subhrajit Dutta & Golam Kabir, 2021. "Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network," Sustainability, MDPI, vol. 13(3), pages 1-24, January.
    6. Bibartiu, Otto & Dürr, Frank & Rothermel, Kurt & Ottenwälder, Beate & Grau, Andreas, 2021. "Scalable k-out-of-n models for dependability analysis with Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    7. Adumene, Sidum & Khan, Faisal & Adedigba, Sunday & Zendehboudi, Sohrab & Shiri, Hodjat, 2021. "Dynamic risk analysis of marine and offshore systems suffering microbial induced stochastic degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    8. DeJesus Segarra, Jonathan & Bensi, Michelle & Modarres, Mohammad, 2021. "A Bayesian Network Approach for Modeling Dependent Seismic Failures in a Nuclear Power Plant Probabilistic Risk Assessment," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    9. Bensi, Michelle & Kiureghian, Armen Der & Straub, Daniel, 2013. "Efficient Bayesian network modeling of systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 200-213.
    10. Cai, Zhongcheng & Liu, Yong & Shen, Ye & Skitmore, Martin & Yu, Shiwang, 2025. "Addressing community oppositions in biowaste incineration infrastructure: Key factors and strategic solutions for a sustainable circular bioeconomy," Socio-Economic Planning Sciences, Elsevier, vol. 99(C).
    11. Kim, Dong-Seok & Ok, Seung-Yong & Song, Junho & Koh, Hyun-Moo, 2013. "System reliability analysis using dominant failure modes identified by selective searching technique," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 316-331.
    12. Guo, Shujie & Yu, Yun & Wei, Wei & Zhang, Shuai & Liu, Jing & Yu, Xinli, 2026. "Nuclear power plant accident risk assessment method research based on bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 268(C).
    13. Paglioni, Vincent P. & Groth, Katrina M., 2022. "Dependency definitions for quantitative human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    14. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    15. Hao, Zhifeng & Yeh, Wei-Chang, 2025. "Applying incremental learning in binary-addition-tree algorithm in reliability analysis of dynamic binary-state networks," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    16. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    17. Ye, Tianyuan & Liu, Linlin & Zhou, Yuan & Gao, Haiyang, 2026. "Effective Noisy-matrix of Bayesian network for scalable m-consecutive-k-out-of-n: F models with overlapping from -1 to k-1," Reliability Engineering and System Safety, Elsevier, vol. 265(PA).
    18. Dao, Uyen & Sajid, Zaman & Khan, Faisal & Zhang, Yahui, 2023. "Dynamic Bayesian network model to study under-deposit corrosion," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    19. Yeh, Wei-Chang, 2025. "Enhancing reliability calculation for one-output k-out-of-n binary-state networks using a new BAT," Reliability Engineering and System Safety, Elsevier, vol. 257(PA).
    20. Yeh, Wei-Chang, 2020. "A new method for verifying d-MC candidates," Reliability Engineering and System Safety, Elsevier, vol. 204(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:156:y:2016:i:c:p:134-147. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.