Study on failure pressure and life prediction of defective pipelines based on numerical simulation

In order to further control the running state of water supply and drainage pipeline, and provide reference for later maintenance. This study used Abaqus to construct simulation models for pipelines of X52, X65, and X80 grades under various corrosion defects, analyzing the variations in pipeline failure pressure due to corrosion depth, length, and width. Additionally, it proposed a predictive model for the failure pressure of corroded pipelines and utilized a BP neural network model to assess the remaining service life of corroded pipelines. The results show that the numerical model and strength evaluation, when compared with actual burst results, have a minimum relative error of only 1.45% in simulation. The failure pressure of flawed pipes diminishes as the corrosion depth and length increase, but remains relatively constant with corrosion width. Meanwhile, every time the corrosion defect depth coefficient increases by 0.1, the failure pressure decreases by 0.9–2.6 MPa. A formula has been fitted to the corrosion depth and length, with parameters <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>a</mml:mi> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>b</mml:mi> </mml:mrow> </mml:math> , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>c</mml:mi> </mml:mrow> </mml:math> being 1.3141, −0.3053, and 0.5212, respectively, and a correlation coefficient R of 0.977. The BP neural network model exhibits a low mean square error of 0.014, indicating its effectiveness in calculating the remaining lifespan of corroded pipelines.