An ANN‐based failure pressure prediction method for buried high‐strength pipes with stray current corrosion defect

Abstract

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.

Numerical investigation of corroded middle‐high strength pipeline subjected to combined internal pressure and axial compressive loading

“…Artificial neural network (ANN) technology is used in the simulation, abstraction, and simplification of brain function, and it has the functions of learning, association, memory, and a highly nonlinear prediction ability. It has been widely used in burst pressure prediction of oil and gas pipelines 23 . In this study, ANN technology is used to predict the burst pressure of corroded steel pipelines.…”

“…It has been widely used in burst pressure prediction of oil and gas pipelines. 23 In this study, ANN technology is used to predict the burst pressure of corroded steel pipelines. The burst pressure of the pipeline can be predicted based on the corrosion defect parameters and the axial force, by adopting the ANN technology.…”

“…In this study, the transfer function between the input layer and the hidden layer was set to be "logsig", 23 while the transfer functions between the hidden layer and the output layer were set to be "purelin". 23 By comparing several kinds of ANN structures with different numbers of neurons and hidden layers, a neural network suitable for failure pressure prediction was obtained. We determined that 2 hidden layers with 6 neurons are the best choice for the presented problem.…”

Structural Integrity of Steel Pipeline with Clusters of Corrosion Defects

“…The effective depth of the metal loss combined from C3D3 and C3D4 calculated according to Equation (4) is equal to dnm = 4.0 mm. The results of the plastic burst pressure calculations according to Equations (5) and (6), in the three analysed cases of colonies of the volumetric defects, are summarized in Table 4. Table 4, it can be concluded that rupture pressure calculated according to Det Norske Veritas Recommended practice for every analysed group of flaws is in the difference range of 2 bar compared to the assessment of the individual features.…”

“…However, a lot of studies deal with investigation of strength and structural integrity of steel pipelines with wall metal losses and longitudinally-oriented grooves similar to cracks using different methodologies, for instance, the latest publications applying a finite element method [ 4 ] and a linear elastic fracture failure mode [ 5 ]. A rupture pressure prediction model for steel tubes affected by the stray current corrosion based on artificial neural network was applied in [ 6 ].…”