Research on predicting external corrosion rate of buried pipeline guided by physical mechanism

Buried pipelines are prone to external corrosion perforation, and leakage during service. Accurate prediction of external corrosion rate is of great significance for formulating reasonable pipeline maintenance strategies. By sorting out the factors affecting the external corrosion rate of pipelines, soil properties, stray currents, cathodic protection, and physicochemical properties were identified as the main influencing factors, and 60 sets of relevant external corrosion data were collected along a gathering pipeline in a certain region of China. Subsequently, corrosion information was extracted through multi-physics and data-driven approaches, and a Physics Guided Neural Network (PGNN) model based on physical mechanism guidance was constructed. On the basis of the conventional loss function, this model introduces physical mechanism constraints as penalty terms, adjusts corrosion factors and retrains the model to ensure that the training direction conforms to the corrosion mechanism. Genetic Algorithm (GA) is used to optimize hyperparameters, and the GA-PGNN corrosion rate prediction model is formed. Finally, Shapley Additive Explanations (SHAP) analysis quantifies to measure the influence of corrosion characteristics on corrosion rate from both global and local perspectives. The results demonstrate that compared to conventional Back Propagation (BP) and GABP models, the GA-PGNN model achieves superior performance with Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and correlation coefficient (R²) of 3.71, 1.51×10^-5, and 0.9935, respectively. The GA-PGNN model exhibits smaller and more balanced mean absolute SHAP values, indicating consistent dependency on diverse corrosion factors and effective utilization of information from each factor. Conversely, both conventional BP and GA-BP models fail to capture accurate corrosion mechanisms, occasionally yield conclusions contradictory to physical principles, and predictions show significant randomness and instability. The GA-PGNN framework provides actionable insights for enhancing the integrity management of buried pipelines.