Data Analysis and Knowledge Mining of Machine Learning in Soil Corrosion Factors of the Pipeline Safety

Abstract

The purpose of this research is to enhance the ability of data analysis and knowledge mining in soil corrosion factors of the pipeline. According to its multifactor characteristics, the rough set algorithm is directly used to analyze and process the observation data without considering any prior information. We apply rough set algorithm to delete the duplicate same information and redundant items and simplify the condition attributes and decision indicators from the decision table. Combined with the simplified index, the decision tree method is used to analyze the root node and branch node of it, and the knowledge decision model is constructed. With the Python machine learning language and PyCharm Community Edition software, the algorithm functions of rough set and decision tree are realized, so as to carry out artificial intelligence analysis and judgment of the soil corrosion factor data in pipeline. Taking the area of loam soil corrosion as an example, the data analysis and knowledge mining of its multifactors original data are carried out through the model. The example verifies that the evaluation and classification rules of the model meet the requirements, and there are no problems such as inconsistency and heterogeneity. It provides decision-making service and theoretical basis for the soil corrosion management of pipeline.

Walk-Through Corrosion Assessment of Slurry Pipeline Using Machine Learning

“…To estimate the corrosion defect depth growth of aged pipelines, Ossai adopts a data-driven machine learning approach, relying on techniques such as principal component analysis (PCA), particle swarm optimization (PSO), feed-forward artificial neural network (FFANN), gradient boosting machine (GBM), random forest (RF), and deep neural network (DNN), to estimate the growth of corrosion defect depth in aged pipelines [10]. Roy et al use the gradient boosting regressor to predict corrosion resistance in alloys with multiple principal elements [22], while Zhao et al suggest using rough set and decision tree methods to analyze pipeline soil corrosion [23]. To model experimental data of time-varying corrosion rates in mild steel specimens when corrosion inhibitors are added to the system at varying concentrations and dose schedules, Aghaaminiha et al perform regression with several ML algorithms, ultimately finding random forest to be the best option [24].…”