Prediction of Failure Pressure of Sulfur-Corrosion-Defective Pipelines Based on GABP Neural Networks.

This study systematically investigates the degradation and failure prediction of pipeline materials in sulfur-containing environments, with a particular focus on X52 pipeline steel exposed to high-sulfur environments. Through uniaxial tensile tests to assess mechanical properties, it was found that despite surface corrosion and a reduction in overall structural load-bearing capacity, the intrinsic mechanical properties of X52 steel did not exhibit significant degradation and remained within standard ranges. The Johnson-Cook constitutive model was developed to accurately capture the material's plastic behavior. Subsequently, a genetic algorithm-optimized backpropagation (GABP) neural network was employed to predict the failure pressure of defective pipelines and the corrosion rate in acidic environments, with prediction errors controlled within 5%. By integrating the GABP model with NACE standard methods, a framework for predicting the remaining service life for in-service pipelines operating in sour environments was established. This method provides a novel and reliable approach for pipeline integrity assessment, demonstrating significantly higher accuracy than traditional empirical models and finite element analysis.