With continued increasing construction of both electrified facilities and buried high‐strength pipelines in China, stray current corrosion defects have become an nonignorable threat for these pipelines. A comprehensive investigation on a new failure pressure prediction model for high‐strength pipes with stray current corrosion defects was conducted in this study. The mechanism of stray current corrosion in steel pipes was firstly elaborated in brief. After that, a parameterized finite element model for stress analysis of pipes with external corrosion defects was programmed by APDL code developed by general software ANSYS. By comparing numerical results with full‐scale experimental results, both the numerical model and the failure criteria for pipe burst were proven to be reasonable. Based on the finite element model, parametric analysis was performed using a calculation matrix set by orthogonal testing method to investigate the effects of three main dimensionless factors, that is, ratio of pipe diameter to wall thickness, nondimensional corrosion defect length, and nondimensional corrosion defect depth on pipe's failure pressure. Utilizing the parametric analysis results as database, a multilayer feed‐forward artificial neural network (ANN) was developed for failure pressure prediction. By comparison with experimental burst test results and results of previous failure pressure estimation model, the ANN model results were proven to have both high accuracy and efficiency, which could be referenced in residual strength or safety assessment of high‐strength pipes with corrosion defects.

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Corrosion defects are dreadfully damaging to the stability of pipelines. Using the finite element (FE) simulation method, a model of API 5L X65 steel pipeline is established in this work to study its buckling behavior subjected to axial compressive loading. The local buckling state of the pipe at the ultimate axial compressive capacity was captured. Compared with the global compressive strain capacity (CSCglobal), the local compressive strain capacity (CSClocal) is more conservative. Extensive parametric analysis, including approximately 115 FE cases, was conducted to study the influence of the corrosion defect sizes and internal pressure on the corroded pipe’s compressive loading capacity (CLC) and CSC. Results show that the enlarged size of the corrosion defect decreases both the CLC and the CSC of the pipeline, but the CLC almost keeps unchanged as the length of corrosion defects increases. The CLC decreases with the increase of the length of corrosion defects when the length is less than 1.5Dt and greater than 0.7Dt. The CSC drops significantly until the length of the corrosion defect reached 1.8Dt. The deeper the corrosion defect, the smaller the CLC and the CSC. An increase in the width of corrosion defects tends to correspond to a decrease in the CLC and the CSC. With the increase of internal pressure, the CSC of the pipe gets greater while the CLC gets smaller. Based on the 115 FE results, a machine learning model based on support vector regression theory was developed to predict the pipe’s CSC. The regression coefficient between SVR predicted value and FEM actual value is 98.87%, which proves that the SVR model can predict the CSC with high accuracy and efficiency.

Thawing landslide is a common geological disaster in permafrost regions, which seriously threatens the structural safety of oil and gas pipelines crossing permafrost regions. Most of the analytical methods have been used to calculate the longitudinal stress of buried pipelines. These analytical methods are subjected to slope-thaw slumping load, and the elastic characteristic of the soil in a nonlinear interaction behavior is ignored. Also, these methods have not considered the real boundary at both ends of the slope. This study set out to introduce an improved analytical method to accurately analyze the longitudinal strain characteristics of buried pipelines subjected to slope-thaw slumping load. In this regard, an iterative algorithm was based on an ideal elastoplastic model in the pipeline-soil interaction. Based on field monitoring and previous finite element results, the accuracy of the proposed method was validated. Besides, a parametric analysis was conducted to study the effects of wall thickness, internal pressure, ultimate soil resistance, and slope angle on the maximum longitudinal strain of the pipeline. The results from the compression section showed that the pipeline is more likely to yield, indicating an actual situation in engineering. Moreover, the maximum longitudinal tensile and compression strain of pipelines decrease with increasing the wall thickness, internal pressure, ultimate resistance of soil, and slope angle. Finally, based on the pipeline limit state equations in CSA Z662-2007 and CRES which considered the critical compression factor comprehensively, the critical slumping displacements for both tensile and compressive strain failures were derived for reference. The research results attach great significance to the safety of pipeline under slope.

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