Reliability evaluation of corroded pipeline under combined loadings based on back propagation neural network method

Abstract

Corrosion is a common type of defect that causes failure of oil and gas pipelines. Pipelines often bear the combined loadings of axial compression force, bending moment and internal pressure in operation; therefore, it is of great significance to study the reliability of pipeline under different combined loadings. First, this work established a nonlinear finite element model of corroded pipeline for the typical internal corrosion defects of groove shape, and the reliability of the model and the accuracy of the finite element analysis method are verified. Then the burst pressure of pipeline under combined loadings is investigated. Second, this work established the BP network analysis and prediction model. The results of numerical simulation are used as samples to train the neural network. The prediction limit function value is output through the forward simulation function, which is compared with the objective function to judge whether the pipeline is reliable. Finally, back propagation neural network model calculated the reliability of pipeline under different working conditions combined with Monte Carlo method. The effects of load, corrosion depth, axial corrosion length, circumferential corrosion width and variation coefficient of parameters on pipeline reliability are analyzed. It is found that axial force and bending moment affect the reliability of corroded pipeline. Generally, the higher the combined loadings are, the lower the reliability is, but this law will be different due to the influence of corrosion parameters. In addition, the corrosion depth has the greatest influence on the reliability of corroded pipeline, followed by the corrosion width and the corrosion length. The coefficient of variation of corrosion depth has a great influence on the reliability of pipeline with high depth corrosion defect. These conclusions can provide references for the reliability evaluation of pipelines.