Earthquakes and Structures

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Strain demand prediction of buried steel pipeline at strike-slip fault crossings: A surrogate model approach

  • Xie, Junyao (Department of Chemical and Materials Engineering, University of Alberta) ;
  • Zhang, Lu (Department of Chemical and Materials Engineering, University of Alberta) ;
  • Zheng, Qian (National Engineering Laboratory for Pipeline Safety, MOE Key Laboratory of Petroleum Engineering, Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing) ;
  • Liu, Xiaoben (National Engineering Laboratory for Pipeline Safety, MOE Key Laboratory of Petroleum Engineering, Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing) ;
  • Dubljevic, Stevan (Department of Chemical and Materials Engineering, University of Alberta) ;
  • Zhang, Hong (National Engineering Laboratory for Pipeline Safety, MOE Key Laboratory of Petroleum Engineering, Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing)
  • Received : 2019.01.03
  • Accepted : 2021.01.21
  • Published : 2021.01.25
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Abstract

Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensional nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.

Keywords

Acknowledgement

The authors acknowledge the support from National Science Foundation of China (Grant No. 52004314), Tianshan Youth Program 2019Q088, Beijing Natural Science Foundation Project (No. 8214053), Science Foundation of China University of Petroleum, Beijing (No. 2462018YJRC019, No. 2462020YXZZ045) and China Petroleum Science & Technology Innovation Fund (2017D5007-0606). The financial support from the program of China Scholarship Council (No. CSC201906440175) is also acknowledged.

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