English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/76175
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Artificial neural network modeling of atmospheric corrosion in the MICAT project

AuthorsPintos, S.; Queipo, N. V.; Trocónis de Rincón, O.; Rincón, A.; Morcillo, Manuel
Issue Date2000
CitationCorrosion Science 42: 35-52 (2000)
AbstractThis paper presents an Artificial Neural Network (ANN)-based solution methodology for modeling atmospheric corrosion processes from observed experimental values, and an ANN model developed using the cited methodology for the prediction of the corrosion rate of carbon steel in the context of the Iberoamerican Corrosion Map (MICAT) Project, which includes seventy-two test sites in fourteen countries throughout Iberoamerica. The ANN model exhibited superior performance in terms of goodness of fit (sum of square errors) and residual distributions when compared against a classical regression model also developed in the context of this study, and is expected to provide reasonable corrosion rates for a variety of climatological and pollution conditions. Furthermore, the proposed methodology holds promise to be an effective and efficient tool for the construction of analytical models associated with corrosion processes of other metals in the context of the MICAT project, and, in general, in the modeling of corrosion phenomena from experimental data.
Identifiersdoi: 10.1016/S0010-938X(99)00054-2
issn: 0010-938X
Appears in Collections:(CENIM) Artículos
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf15,38 kBAdobe PDFThumbnail
Show full item record
Review this work

Related articles:

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.