Exploitation of Artificial Intelligence Methods for Prediction of Atmospheric Corrosion

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86082130" target="_blank" >RIV/61989100:27240/12:86082130 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27360/12:86082130

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Exploitation of Artificial Intelligence Methods for Prediction of Atmospheric Corrosion

Original language description

The contribution deals with the use of artificial neural networks for prediction of corrosion loss of structural carbon steel. Nowadays there is certain chance to predict a corrosion loss of materials by artificial intelligence methods, especially by neural networks. A model of neural network for prediction of corrosion loss of structural carbon steel based on the input environmental parameters affecting the corrosion of metals in the atmospheric environment (temperature, relative humidity, air pollution by sulphur dioxide and the exposition time) was created. The model enables to predict corrosion loss of steel with a sufficiently small error.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JK - Corrosion and material surfaces

OECD FORD branch

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Project

<a href="/en/project/FR-TI1%2F319" target="_blank" >FR-TI1/319: *Development of New Progressive Tools and Systems of Dependability Control Support of Primary Cooling on Slab Device of Continuous Casting for Quality Improvement of Demanding Flat Products</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2012

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Deffect and Diffusion Forum

ISSN

1662-9507

e-ISSN

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Volume of the periodical

326-328

Issue of the periodical within the volume

1

Country of publishing house

CH - SWITZERLAND

Number of pages

4

Pages from-to

65-68

UT code for WoS article

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EID of the result in the Scopus database

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