Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach

Yuanjie Zhi,Xiaogang Li,Tao Yang,Dawei Zhang,Dongmei Fu

doi:10.3390/met9030383

Abstract

The objective of this paper is to develop an approach to forecast the outdoor atmospheric corrosion rate of low alloy steels and do corrosion-knowledge mining by using a Random Forests algorithm as a mining tool. We collected the corrosion data of 17 low alloy steels under 6 atmospheric corrosion test stations in China over 16 years as the experimental datasets. Based on the datasets, a Random Forests model is established to implement the purpose of the corrosion rate prediction and data-mining. The results showed that the random forests can achieve the best generalization results compared to the commonly used machine learning methods such as the artificial neural network, support vector regression, and logistic regression. In addition, the results also showed that regarding the effect to the corrosion rate, environmental factors contributed more than chemical compositions in the low alloy steels, but as exposure time increases, the effect of the environmental factors will gradually become less. Furthermore, we give the effect changes of six environmental factors (Cl− concentration, SO2 concentration, relative humidity, temperature, rainfall, and pH) on corrosion with exposure time increasing, and the results illustrated that pH had a significant contribution to the corrosion of the entire process. The paper also dealt with the problem of the corrosion rate forecast, especially for changing environmental factors situations, and obtained the qualitative and quantitative results of influences of each environmental factor on corrosion.

Highlights

IntroductionDue to the addition of alloying elements such as Cr, Ni, Cu, P, etc. [1], low alloy steels (abbreviated as LAS) have a better corrosion resistance than carbon steels [2,3]
Due to the addition of alloying elements such as Cr, Ni, Cu, P, etc. [1], low alloy steels have a better corrosion resistance than carbon steels [2,3]
The Random Forests (RF) consists of a multiple trees model, of which each tree would output a prediction result corresponding to the given input features

Summary

Introduction

Due to the addition of alloying elements such as Cr, Ni, Cu, P, etc. [1], low alloy steels (abbreviated as LAS) have a better corrosion resistance than carbon steels [2,3]. [1], low alloy steels (abbreviated as LAS) have a better corrosion resistance than carbon steels [2,3]. The LAS would suffer complex corrosion degradation and eventually fail its service performance, which could result in enormous economic and human-life losses [8]. How to forecast the corrosion status of LAS and further predict the remaining life has an important practical engineering significance. With the development of machine-learning algorithms, many studies have used machine-learning technology to establish the corrosion model and to implement the prediction of the corrosion status [9,10,11,12,13,14,15,16,17]. Kamrunnahar [9,10], Jiang [11], Shirazi [12], and Shi [13]

Objectives

Methods

Results

Conclusion