Numerical analysis on corrosion resistance of mild steel structures
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Date
2013
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Abstract
Corrosion resistances of mild steel specimens according to artificial neural network (ANN) analysis were investigated in the scope of this study. Corrosion rate values were taken into numerical analysis as a result of experimental studies under corrosive aggressive media. Mild steel specimens were selected according to the section type varieties such as box, tube and cornier. All steel specimens were subjected to the aggressive media formed using sodium chloride (NaCl with 99.8 % purity) solutions with 3.5, 5.0 and 7.0 % ratios per one liter distilled water and only distilled water. The reduction in corrosion rate has been observed and considered according to some corrosion loss respects. Corrosion rate prediction models were established between corrosion rate and parameters such as mass loss obtained by experimental studies using ANN. ANNs are computing systems that simulate the biological neural systems of the human brain. In this study, ANN analysis was generated to predict the corrosion rate values after experimental studies. Experimental and predicted values were compared by each other and it is seen that a strong relationship was established between them. © 2012 Springer-Verlag London Limited.
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Carbon steel , Corrosion rate , Materials science , Neural networks , Numerical analysis , Sodium chloride , Soft computing , Steel research , Structure (composition) , Aggressive media , Biological neural systems , Computing system , Corrosion loss , Distilled water , Rate predictions , Section types , Steel specimens , Corrosion resistance