IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v222y2022ics0951832022001065.html
   My bibliography  Save this article

Parallel density scanned adaptive Kriging to improve local tsunami hazard assessment for coastal infrastructures

Author

Listed:
  • Di Maio, F.
  • Belotti, M.
  • Volpe, M.
  • Selva, J.
  • Zio, E.

Abstract

Seismic Probabilistic Tsunami Hazard Assessment (SPTHA) is a framework for calculating the probability that seismically induced tsunami waves exceed a specific threshold height, over a given time span and a specific region (i.e. regional SPTHA) or site (i.e. local SPTHA). To account for the uncertainty of the possible sources, SPTHA must integrate the results of a large number of computationally demanding tsunami simulations

Suggested Citation

  • Di Maio, F. & Belotti, M. & Volpe, M. & Selva, J. & Zio, E., 2022. "Parallel density scanned adaptive Kriging to improve local tsunami hazard assessment for coastal infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
  • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022001065
    DOI: 10.1016/j.ress.2022.108441
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022001065
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2022.108441?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Sun, Zhili & Wang, Jian & Li, Rui & Tong, Cao, 2017. "LIF: A new Kriging based learning function and its application to structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 152-165.
    2. Khakzad, Nima & Cozzani, Valerio, 2020. "Special issue: Quantitative assessment and risk management of Natech accidents," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    3. Tabandeh, Armin & Sharma, Neetesh & Gardoni, Paolo, 2022. "Uncertainty propagation in risk and resilience analysis of hierarchical systems," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2017. "Simulation-based exploration of high-dimensional system models for identifying unexpected events," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 317-330.
    5. Jian, Wang & Zhili, Sun & Qiang, Yang & Rui, Li, 2017. "Two accuracy measures of the Kriging model for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 494-505.
    6. Antonioni, Giacomo & Landucci, Gabriele & Necci, Amos & Gheorghiu, Diana & Cozzani, Valerio, 2015. "Quantitative assessment of risk due to NaTech scenarios caused by floods," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 334-345.
    7. Zhang, Xufang & Wang, Lei & Sørensen, John Dalsgaard, 2019. "REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 440-454.
    8. Landucci, Gabriele & Antonioni, Giacomo & Tugnoli, Alessandro & Cozzani, Valerio, 2012. "Release of hazardous substances in flood events: Damage model for atmospheric storage tanks," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 200-216.
    9. Eric Geist & Tom Parsons, 2014. "Undersampling power-law size distributions: effect on the assessment of extreme natural hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 72(2), pages 565-595, June.
    10. Kameshwar, Sabarethinam & Cox, Daniel T. & Barbosa, Andre R. & Farokhnia, Karim & Park, Hyoungsu & Alam, Mohammad S. & van de Lindt, John W., 2019. "Probabilistic decision-support framework for community resilience: Incorporating multi-hazards, infrastructure interdependencies, and resilience goals in a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    11. Lan, Meng & Gardoni, Paolo & Qin, Rongshui & Zhang, Xiao & Zhu, Jiping & Lo, Siuming, 2022. "Modeling NaTech-related domino effects in process clusters: A network-based approach," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    12. Sotirios A. Argyroudis & Stavroula Fotopoulou & Stella Karafagka & Kyriazis Pitilakis & Jacopo Selva & Ernesto Salzano & Anna Basco & Helen Crowley & Daniela Rodrigues & José P. Matos & Anton J. Schle, 2020. "A risk-based multi-level stress test methodology: application to six critical non-nuclear infrastructures in Europe," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 100(2), pages 595-633, January.
    13. Teixeira, Rui & Nogal, Maria & O’Connor, Alan & Martinez-Pastor, Beatriz, 2020. "Reliability assessment with density scanned adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    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. Caratozzolo, Vincenzo & Misuri, Alessio & Cozzani, Valerio, 2022. "A generalized equipment vulnerability model for the quantitative risk assessment of horizontal vessels involved in Natech scenarios triggered by floods," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    2. Yi, Jiaxiang & Cheng, Yuansheng & Liu, Jun, 2022. "A novel fidelity selection strategy-guided multifidelity kriging algorithm for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. Teixeira, Rui & Martinez-Pastor, Beatriz & Nogal, Maria & O’Connor, Alan, 2021. "Reliability analysis using a multi-metamodel complement-basis approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    4. Dang, Chao & Wei, Pengfei & Faes, Matthias G.R. & Valdebenito, Marcos A. & Beer, Michael, 2022. "Parallel adaptive Bayesian quadrature for rare event estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Misuri, Alessio & Ricci, Federica & Sorichetti, Riccardo & Cozzani, Valerio, 2023. "The Effect of Safety Barrier Degradation on the Severity of Primary Natech Scenarios," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    6. Misuri, Alessio & Landucci, Gabriele & Cozzani, Valerio, 2021. "Assessment of risk modification due to safety barrier performance degradation in Natech events," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    7. Zhang, Jinhao & Gao, Liang & Xiao, Mi, 2020. "A composite-projection-outline-based approximation method for system reliability analysis with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    8. Teixeira, Rui & Nogal, Maria & O’Connor, Alan & Martinez-Pastor, Beatriz, 2020. "Reliability assessment with density scanned adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    9. Wang, Jian & Sun, Zhili & Cao, Runan, 2021. "An efficient and robust Kriging-based method for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    10. Saraygord Afshari, Sajad & Enayatollahi, Fatemeh & Xu, Xiangyang & Liang, Xihui, 2022. "Machine learning-based methods in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    11. Chen, Zequan & Li, Guofa & He, Jialong & Yang, Zhaojun & Wang, Jili, 2022. "Adaptive structural reliability analysis method based on confidence interval squeezing," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    12. Chen, Weidong & Xu, Chunlong & Shi, Yaqin & Ma, Jingxin & Lu, Shengzhuo, 2019. "A hybrid Kriging-based reliability method for small failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 31-41.
    13. Misuri, Alessio & Landucci, Gabriele & Cozzani, Valerio, 2021. "Assessment of safety barrier performance in the mitigation of domino scenarios caused by Natech events," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    14. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2020. "A system active learning Kriging method for system reliability-based design optimization with a multiple response model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    15. Jingkui Li & Wenqi Liu & Yan Zhou & Zhandong Li, 2023. "An active learning Kriging-based method combining the weight information entropy function and the adaptive candidate sample pool," Journal of Risk and Reliability, , vol. 237(4), pages 741-751, August.
    16. Chen, Zequan & Li, Guofa & He, Jialong & Yang, Zhaojun & Wang, Jili, 2022. "A new parallel adaptive structural reliability analysis method based on importance sampling and K-medoids clustering," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    17. Chengning Zhou & Ning-Cong Xiao & Ming J Zuo & Xiaoxu Huang, 2020. "AK-PDF: An active learning method combining kriging and probability density function for efficient reliability analysis," Journal of Risk and Reliability, , vol. 234(3), pages 536-549, June.
    18. Bolin Liu & Liyang Xie, 2020. "An Improved Structural Reliability Analysis Method Based on Local Approximation and Parallelization," Mathematics, MDPI, vol. 8(2), pages 1-13, February.
    19. Li, Junxiang & Chen, Jianqiao, 2019. "Solving time-variant reliability-based design optimization by PSO-t-IRS: A methodology incorporating a particle swarm optimization algorithm and an enhanced instantaneous response surface," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    20. Khakzad, Nima & Van Gelder, Pieter, 2018. "Vulnerability of industrial plants to flood-induced natechs: A Bayesian network approach," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 403-411.

    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:222:y:2022:i:c:s0951832022001065. 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.