Research on Corrosion Rate Prediction Model for Long-Distance Pipelines Based on Gravimetric-CART Fusion

As a classical method for corrosion rate determination, the gravimetric method provides fundamental data for corrosion analysis by measuring material mass loss. However, its reliance on laboratory environments and inability to perform real-time prediction limit its engineering applications. This paper proposes a corrosion rate prediction model based on the CART (Classification and Regres-sion Tree) algorithm, which integrates historical gravimetric data with mul-ti-source environmental parameters to achieve dynamic corrosion rate predic-tion under complex working conditions. Experiments show that the CART mod-el significantly outperforms the traditional gravimetric method in prediction accuracy (MAE=0.05 mm/a) and variable interaction analysis capability (MAE=0.12 mm/a). The study reveals how data-driven methods inherit and ex-tend the core value of the traditional gravimetric method, providing an efficient and low-cost solution for corrosion management in long-distance pipelines.