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

Meng Zhang, Jiatong Ling (), Buyun Tang, Shaohua Dong and Laibin Zhang
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Meng Zhang: College of Safety and Ocean Engineering, China University of Petroleum in Beijing, Beijing 102249, 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
Shaohua Dong: College of Safety and Ocean Engineering, China University of Petroleum in Beijing, Beijing 102249, China
Laibin Zhang: College of Safety and Ocean Engineering, China University of Petroleum in Beijing, Beijing 102249, China

Sustainability, 2022, vol. 14, issue 19, 1-16

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.

Keywords: oil and gas pipeline; landside monitoring; data-driven; pipeline additional stress; XGBoost (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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