Discovering key meteorological variables in atmospheric corrosion through an artificial neural network model

Díaz, Verónica; López, Carlos

doi:10.1016/j.corsci.2006.06.023

Cited by 40 publications

(20 citation statements)

References 15 publications

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“…As the great uncertainty in corrosion data and the limitations in dealing with nonlinear interactive effects of conventional regression method, the statistical learning methods are more widely employed in corrosion researches. As an effective tool to solve complex nonlinear problems, the artificial neural networks (ANN) have been used by many researchers in the study of atmospheric corrosion and obtained good prediction results (Diaz and Lopez 2007;Kenny et al 2009;Vera et al 2017;Wang et al 2006). For example, Pintos et al (2000) built an ANN model for the MICAT atmospheric exposure corrosion test data.…”

Section: Statistical Learning Modelmentioning

confidence: 99%

Atmospheric corrosion prediction: a review

Cai

Zhao

et al. 2020

Corrosion Reviews

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The atmospheric corrosion of metallic materials causes great economic loss every year worldwide. Thus, it is meaningful to predict the corrosion loss in different field environments. Generally, the corrosion prediction method includes three parts of work: the modelling of the corrosive environment, the calibration of the corrosion effects, and the establishment of the corrosion kinetics. This paper gives an overview of the existing methods as well as promising tools and technologies which can be used in corrosion prediction. The basic corrosion kinetic model is the power function model and it is accurate for short-term corrosion process. As for the long-term corrosion process, the general linear models are more appropriate as they consider the protective effect of the corrosion products. Most corrosion effect models correlate the environmental variables, which are characterized by the annual average value in most cases, with corrosion parameters by linear equations which is known as the dose-response function. Apart from these conventional methods, some mathematical and numerical methods are also appropriate for corrosion prediction. The corrosive environment can be described by statistical distributions, time-varying functions and even geographic information system (GIS), while the corrosion effect can be captured via response surface models and statistical learning methods.

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Section: Statistical Learning Modelmentioning

confidence: 99%

Atmospheric corrosion prediction: a review

Cai

Zhao

et al. 2020

Corrosion Reviews

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“…Cottis et al presented the application of ANN methods for pitting corrosion behavior modeling of stainless steel as a function of solution composition and temperature. Diaz and Lopez showed the excellent results obtained by an ANN model applied to model atmospheric corrosion of low‐alloy steel. They presented this technique as a useful tool for prediction of corrosion damage under different climatological and pollution conditions.…”

Section: Introductionmentioning

confidence: 98%

A support vector machine‐based ensemble algorithm for pitting corrosion modeling of EN 1.4404 stainless steel in sodium chloride solutions

Jiménez‐Come

Martín

Matres

2018

Materials & Corrosion

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The resistance to localized corrosion of stainless steel in sodium chloride solutions is evaluated at different conditions. The effects of chloride ion concentration, acidity, and temperature on pitting corrosion resistance are analyzed. In order to develop a model capable of predicting pitting corrosion behavior of EN 1.4404 by an automatic way, a support vector machine‐based ensemble algorithm is proposed. An additional step related to feature selection is included in the model in order to improve the prediction capability. According to the excellent prediction results, the support vector machines (SVM) model is used to perform a sensitivity analysis for the purpose of analyzing the influence of the environmental variables on the breakdown potential and the pitting corrosion status modeling of this grade of stainless steel. Based on this analysis, it can be concluded that the breakdown potential is the main variable to be considered in pitting corrosion modeling whereas temperature is the most important one for breakdown potential modeling.

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“…Recently, one-year and long-term predictions have been performed using models based on an artificial neural network (ANN) [ 19 , 20 , 21 , 22 , 23 ]. Their use is undoubtedly a promising approach in the prediction of atmospheric corrosion.…”

Section: Introductionmentioning

confidence: 99%

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

Panchenko

Маршаков

2017

Materials

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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.

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Discovering key meteorological variables in atmospheric corrosion through an artificial neural network model

Cited by 40 publications

References 15 publications

Atmospheric corrosion prediction: a review

Atmospheric corrosion prediction: a review

A support vector machine‐based ensemble algorithm for pitting corrosion modeling of EN 1.4404 stainless steel in sodium chloride solutions

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

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