IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v221y2022ics0951832022000515.html
   My bibliography  Save this article

Risk assessment of buried gas pipelines based on improved cloud-variable weight theory

Author

Listed:
  • Chen, Yinuo
  • Xie, Shuyi
  • Tian, Zhigang

Abstract

Various risk factors of urban gas pipelines will lead to leakage, significantly impacting the environment, assets, and life safety, thus making pipeline risk prevention and control crucial. However, existing methods cannot sufficiently model the uncertainty in the pipeline risk evaluation process. In addition, the calculation method of factor weights may lead to unrealistic results. In this study, variable weight theory and cloud theory are introduced. Then, a novel method of cloud-variable weight function is proposed to analyze the pipeline's risk level and critical risk factors by establishing a pipeline risk assessment index system. The proposed method fully considers the uncertainty in the evaluation process, resolves the contradiction of existing methods to model the fuzzy concepts accurately, optimizes the weight distribution, and obtains a more scientific and reasonable assessment result. A case study is conducted on a pipeline in Beijing City. The results illustrate that the proposed method is beneficial for helping pipeline operators determine the pipeline risk status and weak links in the pipeline system, thereby providing a basis for risk control and rehabilitation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:221:y:2022:i:c:s0951832022000515
    DOI: 10.1016/j.ress.2022.108374
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022000515
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.108374?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. Qin, Guangyu & Zhang, Meijuan & Yan, Qingyou & Xu, Chuanbo & Kammen, Daniel M., 2021. "Comprehensive evaluation of regional energy internet using a fuzzy analytic hierarchy process based on cloud model: A case in China," Energy, Elsevier, vol. 228(C).
    2. Taleb-Berrouane, Mohammed & Khan, Faisal & Hawboldt, Kelly, 2021. "Corrosion risk assessment using adaptive bow-tie (ABT) analysis," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Wang, Zifeng & Li, Suzhen, 2020. "Data-driven risk assessment on urban pipeline network based on a cluster model," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    4. Su, Yue & Li, Jingfa & Yu, Bo & Zhao, Yanlin & Yao, Jun, 2021. "Fast and accurate prediction of failure pressure of oil and gas defective pipelines using the deep learning model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Abrahamsen, Eirik Bjorheim & Abrahamsen, Håkon Bjorheim & Milazzo, Maria Francesca & Selvik, Jon Tømmerås, 2018. "Using the ALARP principle for safety management in the energy production sector of chemical industry," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 160-165.
    6. Qingyou Yan & Meijuan Zhang & Wei Li & Guangyu Qin, 2020. "Risk Assessment of New Energy Vehicle Supply Chain Based on Variable Weight Theory and Cloud Model: A Case Study in China," Sustainability, MDPI, vol. 12(8), pages 1-21, April.
    7. Russo, Paola & Parisi, Fulvio, 2016. "Risk-targeted safety distance of reinforced concrete buildings from natural-gas transmission pipelines," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 57-66.
    8. Xie, Shuyi & Dong, Shaohua & Chen, Yinuo & Peng, Yujie & Li, Xincai, 2021. "A novel risk evaluation method for fire and explosion accidents in oil depots using bow-tie analysis and risk matrix analysis method based on cloud model theory," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    9. Zhang, Y. & Weng, W.G., 2020. "Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    10. Mottahedi, Adel & Sereshki, Farhang & Ataei, Mohammad & Qarahasanlou, Ali Nouri & Barabadi, Abbas, 2021. "Resilience estimation of critical infrastructure systems: Application of expert judgment," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    11. Dundulis, Gintautas & ŽutautaitÄ—, Inga & Janulionis, Remigijus & UÅ¡puras, Eugenijus & RimkeviÄ ius, Sigitas & Eid, Mohamed, 2016. "Integrated failure probability estimation based on structural integrity analysis and failure data: Natural gas pipeline case," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 195-202.
    12. Liu, Aihua & Chen, Ke & Huang, Xiaofei & Li, Didi & Zhang, Xiaochun, 2021. "Dynamic risk assessment model of buried gas pipelines based on system dynamics," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    13. Brito, A.J. & de Almeida, A.T., 2009. "Multi-attribute risk assessment for risk ranking of natural gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 187-198.
    14. 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.
    15. Yu, Xuchao & Liang, Wei & Zhang, Laibin & Reniers, Genserik & Lu, Linlin, 2018. "Risk assessment of the maintenance process for onshore oil and gas transmission pipelines under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 50-67.
    16. Boulomytis, V.T.G. & Zuffo, A.C. & Imteaz, M.A., 2019. "Detection of flood influence criteria in ungauged basins on a combined Delphi-AHP approach," Operations Research Perspectives, Elsevier, vol. 6(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Xing-lin & Huang, Zong-hou & Ge, Fan-liang & Lin, Wei-dong & Yang, Fu-qiang, 2024. "A probabilistic analysis method for evaluating the safety & resilience of urban gas pipeline network," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    2. Chen, Zhanfeng & Li, Xuyao & Wang, Wen & Li, Yan & Shi, Lei & Li, Yuxing, 2023. "Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Xie, Shuyi & Huang, Zimeng & Wu, Gang & Luo, Jinheng & Li, Lifeng & Ma, Weifeng & Wang, Bohong, 2024. "Combining precursor and Cloud Leaky noisy-OR logic gate Bayesian network for dynamic probability analysis of major accidents in the oil depots," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Aorui Bi & Shuya Huang & Xinguo Sun, 2023. "Risk Assessment of Oil and Gas Pipeline Based on Vague Set-Weighted Set Pair Analysis Method," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    5. Dasgupta, Agnimitra & Johnson, Erik A., 2024. "REIN: Reliability Estimation via Importance sampling with Normalizing flows," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Chen, Yinuo & Tian, Zhigang & He, Rui & Wang, Yifei & Xie, Shuyi, 2023. "Discovery of potential risks for the gas transmission station using monitoring data and the OOBN method," Reliability Engineering and System Safety, Elsevier, vol. 232(C).

    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. 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).
    2. Medeiros, Cristina Pereira & da Silva, Lucas Borges Leal & Alencar, Marcelo Hazin & de Almeida, Adiel Teixeira, 2021. "A new method for managing multidimensional risks in Natural Gas Pipelines based on non-Expected Utility," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. 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).
    4. Zhang, Qiongfang & Xu, Nan & Ersoy, Daniel & Liu, Yongming, 2022. "Manifold-based Conditional Bayesian network for aging pipe yield strength estimation with non-destructive measurements," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    5. Ruiz-Tagle, Andres & Lewis, Austin D. & Schell, Colin A. & Lever, Ernest & Groth, Katrina M., 2022. "BaNTERA: A Bayesian Network for Third-Party Excavation Risk Assessment," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    6. Amaya-Gómez, Rafael & Schoefs, Franck & Sánchez-Silva, Mauricio & Muñoz, Felipe & Bastidas-Arteaga, Emilio, 2022. "Matching of corroded defects in onshore pipelines based on In-Line Inspections and Voronoi partitions," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    7. 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).
    8. 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).
    9. Qing Deng & Kuo Wang & Jiahao Wu & Feng Yu & Huiling Jiang & Lida Huang, 2023. "An integrated model for evaluating the leakage risk of urban gas pipe: a case study based on Chinese real accident data," 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. 116(1), pages 319-340, March.
    10. Wu, Xingguang & Huang, Huirong & Xie, Jianyu & Lu, Meixing & Wang, Shaobo & Li, Wang & Huang, Yixuan & Yu, Weichao & Sun, Xiaobo, 2023. "A novel dynamic risk assessment method for the petrochemical industry using bow-tie analysis and Bayesian network analysis method based on the methodological framework of ARAMIS project," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    11. Liu, Aihua & Chen, Ke & Huang, Xiaofei & Li, Didi & Zhang, Xiaochun, 2021. "Dynamic risk assessment model of buried gas pipelines based on system dynamics," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    12. Li, Weijun & Sun, Qiqi & Zhang, Jiwang & Zhang, Laibin, 2024. "Quantitative risk assessment of industrial hot work using Adaptive Bow Tie and Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    13. Sezer, Sukru Ilke & Camliyurt, Gokhan & Aydin, Muhmmet & Akyuz, Emre & Gardoni, Paolo, 2023. "A bow-tie extended D-S evidence-HEART modelling for risk analysis of cargo tank cracks on oil/chemical tanker," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    14. Xiao, Rui & Zayed, Tarek & Meguid, Mohamed A. & Sushama, Laxmi, 2024. "Improving failure modeling for gas transmission pipelines: A survival analysis and machine learning integrated approach," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    15. Chen, Zhanfeng & Li, Xuyao & Wang, Wen & Li, Yan & Shi, Lei & Li, Yuxing, 2023. "Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    16. Li, Pengyu & Wang, Xiufang & Jiang, Chunlei & Bi, Hongbo & Liu, Yongzhi & Yan, Wendi & Zhang, Cong & Dong, Taiji & Sun, Yu, 2024. "Advanced transformer model for simultaneous leakage aperture recognition and localization in gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    17. Zhou, Jie & Lin, Haifei & Li, Shugang & Jin, Hongwei & Zhao, Bo & Liu, Shihao, 2023. "Leakage diagnosis and localization of the gas extraction pipeline based on SA-PSO BP neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    18. Zhang, Y. & Weng, W.G., 2020. "Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    19. Liu, Xinglei & Liu, Jun & Ren, Kezheng & Liu, Xiaoming & Liu, Jiacheng, 2022. "An integrated fuzzy multi-energy transaction evaluation approach for energy internet markets considering judgement credibility and variable rough precision," Energy, Elsevier, vol. 261(PB).
    20. 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.

    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:221:y:2022:i:c:s0951832022000515. 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.