000			01695nab a2200205 4500
005			20260523194547.0
008			260224s2011 xxu
100	1		_aHalama, M. _950497
100	1		_aKreislova, K. _950498
100	1		_aVan Lysebettens, J. _950499
245	0	0	_aPrediction of atmospheric corrosion of carbon steel using artificial neural network model in local geographical regions
260			_cjun. 2011
270			_a27/06/2011 ; 27/06/2011
300			_a6 p. ; 065004
520			_aTranscripción del resumen del autor. Atmospheric corrosion of metals is a complex, nonlinear process. It involves a large number of interacting and varying factors governed by material composition, form, size, testing procedure, location of exposure, and type of application. Possible environmental factors include temperature, relative humidity, wet-dry patterns, hours of sunshine, pH of rainfall, amount of precipitation, concentration of main pollutants, etc. All factors are apart of the variables in artificial neural network (ANN) modeling. The most important variables are chosen from long-term experiences as elements in the development of a prototype "artificial intelligent sensor"'a model designed for the assessment of atmospheric corrosion of carbon steel under local geographical conditions. The variable impact analysis and 2D maps gave accurate prediction of the atmospheric corrosion of carbon steel. Future climatic scenarios, mainly the calculation of mass losses under different simulated concentrations of sulfur dioxide (SO2) during exposure time, are presented.
581			_a6
773	0		_tCorrosion _g67
942			_cARTICULO _2ddc
999			_c183188 _d183188