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Risk assessment by integrating interpretive structural modeling and Bayesian network, case of offshore pipeline project

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  • Wu, Wei-Shing
  • Yang, Chen-Feng
  • Chang, Jung-Chuan
  • Château, Pierre-Alexandre
  • Chang, Yang-Chi

Abstract

The sound development of marine resource usage relies on a strong maritime engineering industry. The perilous marine environment poses the highest risk to all maritime work. It is therefore imperative to reduce the risk associated with maritime work by using some analytical methods other than engineering techniques. This study addresses this issue by using an integrated interpretive structure modeling (ISM) and Bayesian network (BN) approach in a risk assessment context. Mitigating or managing maritime risk relies primarily on domain expert experience and knowledge. ISM can be used to incorporate expert knowledge in a systematic manner and helps to impose order and direction on complex relationships that exist among system elements. Working with experts, this research used ISM to clearly specify an engineering risk factor relationship represented by a cause–effect diagram, which forms the structure of the BN. The expert subjective judgments were further transformed into a prior and conditional probability set to be embedded in the BN. We used the BN to evaluate the risks of two offshore pipeline projects in Taiwan. The results indicated that the BN can provide explicit risk information to support better project management.

Suggested Citation

  • Wu, Wei-Shing & Yang, Chen-Feng & Chang, Jung-Chuan & Château, Pierre-Alexandre & Chang, Yang-Chi, 2015. "Risk assessment by integrating interpretive structural modeling and Bayesian network, case of offshore pipeline project," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 515-524.
    • Handle: RePEc:eee:reensy:v:142:y:2015:i:c:p:515-524
    DOI: 10.1016/j.ress.2015.06.013
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    Cited by:

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    2. Chen, Yinuo & Xie, Shuyi & Tian, Zhigang, 2022. "Risk assessment of buried gas pipelines based on improved cloud-variable weight theory," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
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    5. Chunchang Zhang & Hu Sun & Yuanyuan Zhang & Gen Li & Shibo Li & Junyu Chang & Gongqian Shi, 2023. "Fire Accident Risk Analysis of Lithium Battery Energy Storage Systems during Maritime Transportation," Sustainability, MDPI, vol. 15(19), pages 1-12, September.
    6. Qazi, Abroon & Dickson, Alex & Quigley, John & Gaudenzi, Barbara, 2018. "Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks," International Journal of Production Economics, Elsevier, vol. 196(C), pages 24-42.
    7. Kraidi, Layth & Shah, Raj & Matipa, Wilfred & Borthwick, Fiona, 2019. "Analyzing the critical risk factors associated with oil and gas pipeline projects in Iraq," International Journal of Critical Infrastructure Protection, Elsevier, vol. 24(C), pages 14-22.
    8. Kraidi, Layth & Shah, Raj & Matipa, Wilfred & Borthwick, Fiona, 2020. "Using stakeholders’ judgement and fuzzy logic theory to analyze the risk influencing factors in oil and gas pipeline projects: Case study in Iraq, Stage II," International Journal of Critical Infrastructure Protection, Elsevier, vol. 28(C).
    9. Nina Shin & Sangwook Park, 2019. "Evidence-Based Resilience Management for Supply Chain Sustainability: An Interpretive Structural Modelling Approach," Sustainability, MDPI, vol. 11(2), pages 1-23, January.
    10. Dosung Kim & Yonghee Kim & Namyong Lee, 2018. "A Study on the Interrelations of Decision-Making Factors of Information System (IS) Upgrades for Sustainable Business Using Interpretive Structural Modeling and MICMAC Analysis," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
    11. Yongbo Li & Bathrinath Sankaranarayanan & D. Thresh Kumar & Ali Diabat, 2019. "Risks assessment in thermal power plants using ISM methodology," Annals of Operations Research, Springer, vol. 279(1), pages 89-113, August.
    12. Yin, Yuanbo & Yang, Hao & Duan, Pengfei & Li, Luling & Zio, Enrico & Liu, Cuiwei & Li, Yuxing, 2022. "Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    13. Yang, Yang & Li, Suzhen & Zhang, Pengcheng, 2022. "Data-driven accident consequence assessment on urban gas pipeline network based on machine learning," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    14. Shengyu Guo & Jiali He & Jichao Li & Bing Tang, 2019. "Exploring the Impact of Unsafe Behaviors on Building Construction Accidents Using a Bayesian Network," IJERPH, MDPI, vol. 17(1), pages 1-15, December.
    15. Huang, Wencheng & Zhang, Yue & Kou, Xingyi & Yin, Dezhi & Mi, Rongwei & Li, Linqing, 2020. "Railway dangerous goods transportation system risk analysis: An Interpretive Structural Modeling and Bayesian Network combining approach," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    16. Alisha Lakra & Shubhkirti Gupta & Ravi Ranjan & Sushanta Tripathy & Deepak Singhal, 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach," Logistics, MDPI, vol. 6(4), pages 1-15, October.
    17. Gaurav Kumar Badhotiya & Gunjan Soni & Vipul Jain & Rohit Joshi & Sameer Mittal, 2022. "Assessing supply chain resilience to the outbreak of COVID-19 in Indian manufacturing firms," Operations Management Research, Springer, vol. 15(3), pages 1161-1180, December.
    18. Hong, Bingyuan & Shao, Bowen & Guo, Jian & Fu, Jianzhong & Li, Cuicui & Zhu, Baikang, 2023. "Dynamic Bayesian network risk probability evolution for third-party damage of natural gas pipelines," Applied Energy, Elsevier, vol. 333(C).
    19. Hosseini, Seyedmohsen & Barker, Kash, 2016. "A Bayesian network model for resilience-based supplier selection," International Journal of Production Economics, Elsevier, vol. 180(C), pages 68-87.
    20. Gaogeng Zhu & Guoming Chen & Jingyu Zhu & Xiangkun Meng & Xinhong Li, 2022. "Modeling the Evolution of Major Storm-Disaster-Induced Accidents in the Offshore Oil and Gas Industry," IJERPH, MDPI, vol. 19(12), pages 1-27, June.
    21. Chen, Qian & Zuo, Lili & Wu, Changchun & Cao, Yankai & Bu, Yaran & Chen, Feng & Sadiq, Rehan, 2021. "Supply reliability assessment of a gas pipeline network under stochastic demands," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    22. Jayaraman, Deepan & Ramu, Palaniappan, 2023. "L-moments and Bayesian inference for probabilistic risk assessment with scarce samples that include extremes," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    23. Qazi, Abroon & Quigley, John & Dickson, Alex & Ekici, Şule Önsel, 2017. "Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies," European Journal of Operational Research, Elsevier, vol. 259(1), pages 189-204.

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