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Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network

Author

Listed:
  • Weidong Sun

    (Pipeline Technology and Safety Research Center, China University of Petroleum-Beijing, Beijing 102249, China
    Pipe Network Group (Xuzhou) Pipeline Inspection and Testing Co., Ltd., Xuzhou 221008, China)

  • Jialu Zhang

    (Pipeline Technology and Safety Research Center, China University of Petroleum-Beijing, Beijing 102249, China)

  • Yasir Mukhtar

    (Pipeline Technology and Safety Research Center, China University of Petroleum-Beijing, Beijing 102249, China
    Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China)

  • Lili Zuo

    (Pipeline Technology and Safety Research Center, China University of Petroleum-Beijing, Beijing 102249, China)

  • Shaohua Dong

    (Pipeline Technology and Safety Research Center, China University of Petroleum-Beijing, Beijing 102249, China
    Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China)

Abstract

As the lifeline of offshore oil and gas production, a submarine pipeline requires regular safety evaluations with proper maintenance according to the evaluation results. At present, the safety factors based on regional-level commonly used factors in engineering are too many, and this leads to conservative evaluation results with a low acceptance of defects. In this paper, a risk factor evaluation index system for submarine pipeline defects is constructed through an analytic hierarchy process (AHP), and the original safety factors are corrected to achieve accurate evaluations for submarine pipeline safety. By constructing a radial basis neural network (RBFNN), the fast calculation of safety factors for other pipeline defects can be realized. Through comparison, it was found that the values obtained by the machine training were in good agreement with the real values, which reflects the accuracy of the model and provides a basis for the repair of a defective pipeline.

Suggested Citation

  • Weidong Sun & Jialu Zhang & Yasir Mukhtar & Lili Zuo & Shaohua Dong, 2023. "Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network," Sustainability, MDPI, vol. 15(17), pages 1-13, August.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12724-:d:1222815
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