Study on evaluation model of rate grade of soil corrosion for pipeline

The purpose of this research is to improve the analysis of grade evaluation of soil corrosion rate for pipeline. With the characteristics of the existing mathematical models and the factors of pipeline soil corrosion, a combined mathematical model of the rough set, the decision tree and support vector machine (RS-DT-SVM) is proposed to evaluate the grade of soil corrosion rate for pipeline. This method utilizes the rough set (RS) and decision tree (DT) to manage by standardization analysis of the data of pipeline soil corrosion in the early stage. According to the characteristics of solving the problems of over learning, local minima, nonlinearity and dimension disaster, support vector machine (SVM) is used to build the intelligent evaluation of rate grade of soil corrosion. With algorithm characteristics of machine learning, it can be built without determining the specific function expression. Taking the actual and objective corrosion data of loam as an example, the model has a relative superiority in the evaluation grade analysis of multi-factor control system. It also provides a method guidance of evaluation grade of soil corrosion rate for pipeline.