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Data-driven risk assessment on urban pipeline network based on a cluster model

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  • Wang, Zifeng
  • Li, Suzhen

Abstract

The existing infrastructure system has collected multi-source attribute data for the Urban Pipeline Network (UPN) from sensors, while the failure records are much rare in a relatively short period. Supervised machine learning model confronts challenges for UPN risk assessment because of dramatic class imbalance between the major normal samples and the minor abnormal failures. The previous works apply the clustering as a pre-processing tool, by classifying samples to clusters for simplicity of the following manual risk level rating or failure prediction. In contrast, this work proposes a method based on clustering and statistical test for evaluating risk state via small volume of historical failure records. This framework achieves totally data-driven risk assessment in an unsupervised way, avoiding expensive manual labeling for pipelines’ risk states and probabilities estimation of elementary events in the probabilistic Bayesian approach. A case study is finally conducted on an urban gas pipeline network from a city, consisting of more than 13,000 pipelines (over 1700 km). The results demonstrate that the cluster of pipelines with highest risk have seven times higher accident rate than the lowest one. This method is hopeful to support decision-making regarding to routine inspection and restoration plan for the pipeline networks.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:reensy:v:196:y:2020:i:c:s0951832018315552
    DOI: 10.1016/j.ress.2019.106781
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    References listed on IDEAS

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    1. 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.
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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).
    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. Ramos-Salgado, Cristóbal & Muñuzuri, Jesús & Aparicio-Ruiz, Pablo & Onieva, Luis, 2021. "A decision support system to design water supply and sewer pipes replacement intervention programs," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. 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).
    6. Braga, Joaquim A.P. & Andrade, António R., 2021. "Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Ramos-Salgado, Cristóbal & Muñuzuri, Jesús & Aparicio-Ruiz, Pablo & Onieva, Luis, 2022. "A comprehensive framework to efficiently plan short and long-term investments in water supply and sewer networks," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    8. Kumar, Sourabh & Barua, Mukesh Kumar, 2022. "A modeling framework and analysis of challenges faced by the Indian petroleum supply chain," Energy, Elsevier, vol. 239(PE).
    9. Yao, Lizhong & Zhang, Yu & He, Tiantian & Luo, Haijun, 2023. "Natural gas pipeline leak detection based on acoustic signal analysis and feature reconstruction," Applied Energy, Elsevier, vol. 352(C).

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