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Description
  • Corrosion of structural metals exposed under atmospheric conditions depends on various factors such as local temperature, relative humidity, amount of precipitation, pH of rainfall, concentration of main pollutants (SO42-, Cl-, NOX, O3, etc.) and exposition time. An application of Artificial Intelligence in the form of Neural Networks (ANN) seems to be sophisticated way for prediction of atmospheric corrosion of structural metals. In cooperation of SVÚOM Ltd. Prague and TU Kosice prototype using live prediction artificial neural models was developed to assess corrosion rate of carbon steel based on long-term exposure data.
  • Corrosion of structural metals exposed under atmospheric conditions depends on various factors such as local temperature, relative humidity, amount of precipitation, pH of rainfall, concentration of main pollutants (SO42-, Cl-, NOX, O3, etc.) and exposition time. An application of Artificial Intelligence in the form of Neural Networks (ANN) seems to be sophisticated way for prediction of atmospheric corrosion of structural metals. In cooperation of SVÚOM Ltd. Prague and TU Kosice prototype using live prediction artificial neural models was developed to assess corrosion rate of carbon steel based on long-term exposure data. (en)
Title
  • Atmospheric corrosion of structural metals – methods of prediction of corrosion attack
  • Atmospheric corrosion of structural metals – methods of prediction of corrosion attack (en)
skos:prefLabel
  • Atmospheric corrosion of structural metals – methods of prediction of corrosion attack
  • Atmospheric corrosion of structural metals – methods of prediction of corrosion attack (en)
skos:notation
  • RIV/25794787:_____/08:#0000321!RIV11-MSM-25794787
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM2579478701)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 357248
http://linked.open...ai/riv/idVysledku
  • RIV/25794787:_____/08:#0000321
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Precipitation; artificial inteligence; neural network; karbon steel; long-term exposure (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [EF629D299F67]
http://linked.open...v/mistoKonaniAkce
  • Edinburgh
http://linked.open...i/riv/nazevZdroje
  • Eurocorr 2008
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Kreislová, Kateřina
  • Halama, M.
  • Knotková, Dagmar
  • Lesebettens, J. V.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
issn
  • 0043-1648
number of pages
http://purl.org/ne...btex#hasPublisher
  • Neuveden
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