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A Data-Driven Based Method for Pipeline Additional Stress Prediction Subject to Landslide Geohazards

Author

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  • Meng Zhang

    (College of Safety and Ocean Engineering, China University of Petroleum in Beijing, Beijing 102249, China
    Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China
    CNPC International Pipeline Company, Beijing 102206, China)

  • Jiatong Ling

    (School of Engineering, The University of British Columbia, Kelowna, BC V1V 1V7, Canada)

  • Buyun Tang

    (Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China
    College of Mechanical and Transportation Engineering, China University of Petroleum in Beijing, Beijing 102249, China)

  • Shaohua Dong

    (College of Safety and Ocean Engineering, China University of Petroleum in Beijing, Beijing 102249, China
    Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China)

  • Laibin Zhang

    (College of Safety and Ocean Engineering, China University of Petroleum in Beijing, Beijing 102249, China
    Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China)

Abstract

Pipelines that cross complex geological terrains are inevitably threatened by natural hazards, among which landslide attracts extensive attention when pipelines cross mountainous areas. The landslides are typically associated with ground movements that would induce additional stress on the pipeline. Such stress state of pipelines under landslide interference seriously damage structural integrity of the pipeline. Up to the date, limited research has been done on the combined landslide hazard and pipeline stress state analysis. In this paper, a multi-parameter integrated monitoring system was developed for the pipeline stress-strain state and landslide deformation monitoring. Also, data-driven models for the pipeline additional stress prediction was established. The developed predictive models include individual and ensemble-based machine learning approaches. The implementation procedure of the predictive models integrates the field data measured by the monitoring system, with k-fold cross validation used for the generalization performance evaluation. The obtained results indicate that the XGBoost model has the highest performance in the prediction of the additional stress. Besides, the significance of the input variables is determined through sensitivity analyses by using feature importance criteria. Thus, the integrated monitoring system together with the XGBoost prediction method is beneficial to modeling the additional stress in oil and gas pipelines, which will further contribute to pipeline geohazards monitoring management.

Suggested Citation

  • Meng Zhang & Jiatong Ling & Buyun Tang & Shaohua Dong & Laibin Zhang, 2022. "A Data-Driven Based Method for Pipeline Additional Stress Prediction Subject to Landslide Geohazards," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:11999-:d:922470
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    References listed on IDEAS

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    1. Shucong Liu & Hongjun Wang & Rui Li & Beilei Ji, 2022. "A Novel Feature Identification Method of Pipeline In-Line Inspected Bending Strain Based on Optimized Deep Belief Network Model," Energies, MDPI, vol. 15(4), pages 1-19, February.
    2. Haibat Ali & Jae-ho Choi, 2019. "A Review of Underground Pipeline Leakage and Sinkhole Monitoring Methods Based on Wireless Sensor Networking," Sustainability, MDPI, vol. 11(15), pages 1-24, July.
    3. Marie-Hélène Roy & Denis Larocque, 2012. "Robustness of random forests for regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 993-1006, December.
    4. Wenhan Xu & Hong Xu & Jie Chen & Yanfei Kang & Yuanyuan Pu & Yabo Ye & Jue Tong, 2022. "Combining Numerical Simulation and Deep Learning for Landslide Displacement Prediction: An Attempt to Expand the Deep Learning Dataset," Sustainability, MDPI, vol. 14(11), pages 1-20, June.
    5. Hao Pu & Jia Xie & Paul Schonfeld & Taoran Song & Wei Li & Jie Wang & Jianping Hu, 2021. "Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
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    1. Huseyin Ayhan Yavasoglu & Ilhami Unal & Ahmet Koksoy & Kursad Gokce & Yusuf Engin Tetik, 2023. "Long-Range Wireless Communication for In-Line Inspection Robot: 2.4 km On-Site Test," Sustainability, MDPI, vol. 15(10), pages 1-17, May.

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