Coupon 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.
Data mining was introduced and 178 sets of alternating current (AC) corrosion data were collected from published data and our research group. The AC current density (J AC ), direct current (DC), current density (J DC ), the ratio of AC/DC current density (J AC /J DC ), and cathodic protection potential (IR-free potential, E IR-free ) were defined as the input feature and the priority of feature subset was ranked as (J AC , J DC ) > (J DC , J AC /J DC ) > (J AC , E IR-free ) >J AC > (J AC / J DC , E IR-free ) > J AC /J DC . Then, based on the different feature subsets, the AC corrosion rate model was established by the random forest algorithm, and the generalization ability of the model was verified on the test data respectively.Finally, the mapping relationship between AC, DC parameters, and corrosion rates was presented and the limiting values of (J AC , J DC ) and (J DC , J AC /J DC ) were given.
The application of machine learning (ML) to corrosion research has become an important trend in corrosion science in recent years. In this paper, the feature extraction method for corrosion data and the ML algorithms commonly used (including artificial neural networks, support vector machines, ensemble learning and other widely used algorithms) in corrosion field is introduced. Then, the characteristics of different algorithms and their application scenarios in the corrosion prediction are summarized. Finally, the development trend of ML in material corrosion field is prospected.
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