Research on corrosion assessment model for buried steel pipelines under dynamic DC stray current based on machine learning

Abstract

AbstractCoupon tests on the pipeline subjected to metro dynamic DC interference were conducted. The pipe‐to‐soil from 78 sites including off‐potentials (Eoff), on‐potentials (Eon), and corrosion rate data were obtained, and then the dynamic fluctuation parameters of pipe‐to‐soil potentials were analyzed statistically. Further, the Pearson correlation between dynamic statistical parameters of pipe‐to‐soil potentials and corrosion rates was analyzed and showed that the statistical parameter of “roff ⩾ −0.85 V” and “mean[Eoff,cmax]” have a greater correlation with corrosion rate than others. Based on the statistical parameters of Eoff and the machine learning method, the optimal feature subset of the corrosion rate model was selected; meanwhile, it is proved that the negative shift of Eoff plays an important role in the prediction of corrosion rate. Finally, the corrosion rate model for buried steel pipeline under dynamic DC interference was established by random forest (rfr) method on 66 training data, then generalization performance of the model was verified on 12 test data. The results show that mean absolute error and R2 are 0.0358 mm/a and 0.56, respectively, on test data.