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Hybrid Bayesian network-based landslide risk assessment method for modeling risk for industrial facilities subjected to landslides

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  • Lan, Meng
  • Zhu, Jiping
  • Lo, Siuming

Abstract

Industrialization exposes more petrochemical facilities in the slope-industrial interfaces (SIIs) to the impact range of landslides, demanding practical assessment approaches for addressing associated damage and its potential consequences. However, accurately predicting the expansion and upgrade of landslide hazards in industrial plants is challenging, involving a series of cascading-event trigger-response analyses. Accordingly, this paper develops a hybrid Bayesian network-based landslide risk assessment (HBN-LRA) model to evaluate the landslide risk on storage tanks in SIIs. This model decomposes the landslide risk into three submodules: slope stability, failure of targets, and proactive remedial measures and updates. It transmits the landslide risk through conditional dependence between subsystems. The results of applying the HBN-LRA model toward quantifying the landslide risk in a typical SII area indicate that the distance from the slope in the system risk factors directly determines storage tank damage. Landslide risk is sensitive to geological and geomorphic conditions, such as soil depth, cohesion, and unit weight; their relative importance all exceed 0.15. Tests on the case slope demonstrate that reducing the drainage paving distance from 10 to 8 m can improve slope stability by 50%. This result highlights the potential of the proactive remedial module in evaluating and designing slope drainage systems.

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  • Lan, Meng & Zhu, Jiping & Lo, Siuming, 2021. "Hybrid Bayesian network-based landslide risk assessment method for modeling risk for industrial facilities subjected to landslides," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021003707
    DOI: 10.1016/j.ress.2021.107851
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    References listed on IDEAS

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    Cited by:

    1. Hunte, Joshua L. & Neil, Martin & Fenton, Norman E., 2024. "A hybrid Bayesian network for medical device risk assessment and management," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. 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).
    3. 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).
    4. Lan, Meng & Gardoni, Paolo & Weng, Wenguo & Shen, Kaixin & He, Zhichao & Pan, Rongliang, 2024. "Modeling the evolution of industrial accidents triggered by natural disasters using dynamic graphs: A case study of typhoon-induced domino accidents in storage tank areas," Reliability Engineering and System Safety, Elsevier, vol. 241(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. Li, Yajun & Qian, Cheng & Zhang, Bin & Xu, Nengxiong, 2024. "Reliability and landslide consequence analysis of long heterogeneous soil infrastructure slopes: A parallel computing investigation," Reliability Engineering and System Safety, Elsevier, vol. 251(C).

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