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

Abstract

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An Empirical Equation for Failure Pressure Prediction of High Toughness Pipeline with Interacting Corrosion Defects Subjected to Combined Loadings Based on Artificial Neural Network

“…Xu et al conducted parametric studies using FEM to generate a training dataset for an ANN model to accurately predict the failure pressure of a high toughness pipe with a relative error of less than 2% [33]. Their results were supported by Lu and Liang [34] and Gholami et al [35], who developed an ANN model based on data generated from FEA. It was proven that an ANN is highly capable of predicting the failure pressure of a corroded pipeline accurately.…”

“…There was an insignificant error between the experimental values and results obtained from the trained ANN. This approach was also followed by Lu and Liang [34], and Gholami et al [35], who studied the effects of defect geometry on the failure pressure of a corroded pipe using FEM and used the data to train an ANN model. As such, FEM is a reliable method for obtaining training data for the development of an ANN.…”

Experimental and numerical study of shallow‐buried high‐density polyethylene pipeline under collapse touchdown impact

“…Considering the accuracy and efficiency of the calculation, the grid used an eight-node linear hexahedral linear reduction integration unit (C3D8R). 30 The quality of the mesh was then verified using the mesh check tool.…”