Application of the Random Forest Algorithm of Corrosion Losses of Aluminum for the First Year of Exposure in Various Regions of the World

M. A. Gavryushina; Гаврюшина М. А.; A. I. Marshakov; Маршаков А. И.; Yu. M. Panchenko; Панченко Ю. М.

doi:10.31857/S0044185623700249

Application of the Random Forest Algorithm of Corrosion Losses of Aluminum for the First Year of Exposure in Various Regions of the World

Авторлар: Gavryushina M.A.¹, Marshakov A.I.¹, Panchenko Y.M.¹
Мекемелер:
1. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 119071, Moscow, Russia
Шығарылым: Том 59, № 1 (2023)
Беттер: 99-109
Бөлім: ФИЗИКО-ХИМИЧЕСКИЕ ПРОБЛЕМЫ ЗАЩИТЫ МАТЕРИАЛОВ
URL: https://journal-vniispk.ru/0044-1856/article/view/139727
DOI: https://doi.org/10.31857/S0044185623700249
EDN: https://elibrary.ru/SZJHKA
ID: 139727

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Аннотация

Using the random forest algorithm (RF), two models are obtained for predicting first-year corrosion losses K1 of aluminum in an open atmosphere in various regions of the world. The RF1 model was obtained using the combined databases of the international programs ISO CORRAG and MICAT and tests in Russia and is intended for evaluation of K1 in different types of atmosphere in different regions of the world. The model makes it possible to predict K1 only in the continental regions of the world. For all types of atmospheres, a comparison was made of the accuracy of the prediction of K1 according to the RF1 model and the dose-response function (DRF) presented in the ISO 9223 standard. For continental sites, a comparison of the reliability of the prediction is given by the RF2 model and the dose-response functions presented in ISO 9223 and the new DRF. It is shown that the reliability of predictions for both RF models is significantly better than using dose-response functions.

Негізгі сөздер

atmospheric corrosion, machine learning, random forest, aluminum

Әдебиет тізімі

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