Prediction of First-Year Corrosion Losses of Carbon Steel and Zinc in Continental Regions

Abstract

Dose-response functions (DRFs) developed for the prediction of first-year corrosion losses of carbon steel and zinc (K1) in continental regions are presented. The dependences of mass losses on SO2 concentration, K = f([SO2]), obtained from experimental data, as well as nonlinear dependences of mass losses on meteorological parameters, were taken into account in the development of the DRFs. The development of the DRFs was based on the experimental data from one year of testing under a number of international programs: ISO CORRAG, MICAT, two UN/ECE programs, the Russian program in the Far-Eastern region, and data published in papers. The paper describes predictions of K1 values of these metals using four different models for continental test sites under UN/ECE, RF programs and within the MICAT project. The predictions of K1 are compared with experimental K1 values, and the models presented here are analyzed in terms of the coefficients used in the models.

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

“…In recent years, 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]. For example, Kamrunnahar [9,10], Jiang [11], Shirazi [12], and Shi [13] used artificial neural networks (abbreviated as ANN) to build the corrosion behavior model [9,10] or prediction model [11][12][13] of one specific alloy material.…”

“…Fang established a corrosion loss prediction model of metallic materials in an atmospheric environment based on support vector regression (abbreviated as SVR) [14]. Panchenko studied the law of corrosion as a function of the exposure time, and according to the mining law, he utilized the power function [15] and power-linear [16] function to implement the long-term prediction of corrosion. Shi analyzed and built a prediction model of the corrosion density data using a hidden Markov chain method [17].…”

“…Shi analyzed and built a prediction model of the corrosion density data using a hidden Markov chain method [17]. However, in most of the above works, the objective was just one single material [9][10][11][12][13][14]17] or had one single input variable [15,16]. When extended to the corrosion prediction of various materials in a variety of environmental conditions, there still exist some problems in the above methods, leading to poor performance on the datasets.…”

A Machine Learning‐Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel

“…e quantitative relationships of environmental factors on the corrosion process were presented using the basic linear model [18,19], the basic log-linear model [19][20][21], and dose-response functions [22][23][24]. Empirical equations to calculate the atmospheric corrosion rate were also proposed by some studies [19,21,25].…”

Analysis of Environmental Factors Affecting the Atmospheric Corrosion Rate of Low-Alloy Steel Using Random Forest-Based Models

“…For instance, Panchenko et al developed a new dose-response function which provided more accurate prediction results of the first-year corrosion rate than other standard and unified dose-response functions. Quantitative estimations of the effects of each atmosphere corrosivity parameter on corrosion were considered important to develop more accurate dose-response functions [ 19 ]. Through the regression analysis of corrosion data, the in-depth exploration of atmospheric corrosion processes is accelerated.…”