Prediction method for burst pressure of oil and gas pipelines based on GA-BP algorithm

As an important infrastructure for energy transmission, oil and gas pipelines directly affect industrial production, residents' lives, and sustainable economic development; Accurately predicting the burst pressure of oil and gas pipelines is crucial for the safety assessment of in-service pipeline systems. This article conducts experimental research and collects relevant experimental data to analyze and evaluate industry standard specification prediction formulas, revealing the influence mechanism of material properties (strain hardening, yield to tensile strength ratio) and structural form (diameter ratio) on prediction results; At the same time, to overcome the shortcomings of conventional empirical formulas in handling nonlinear variables, a data-driven algorithm based on machine learning is introduced to analyze and optimize input variables for prediction, and comprehensively evaluate the accuracy indicators of various prediction formula models. The results show that traditional empirical formulas are based on certain design criteria (Tresca, Mises) and can predict relatively accurately within a certain range, but they do not have absolute universality. Especially for oil and gas pipelines with defects, the prediction error is relatively large. Machine learning algorithms have significantly improved the accuracy of predicting burst pressure. Therefore, analyzing data influence weights, simplifying input variables, and combining shallow algorithm processing methods have certain reference significance for predicting burst pressure in oil and gas pipelines.