Oluwole, O.Idusuyi, N.2018-10-162018-10-1620122162-93822162-8424ui_art_oluwole_artificial_2012American Journal of Materials Science 2(3), pp. 62-65http://ir.library.ui.edu.ng/handle/123456789/2573This work presents the artificial neural network(ANN) modeling for sacrificial anode cathodic protection of low carbon steel using Al-Zn-Sn alloys anodes in saline media. Corrosion experiments were used to obtain data for developing a neural network model. The Feed forward Levenberg-Marquadt training algorithm with passive time, pH, conductivity,% metallic composition used in the input layer and the corrosion potential measured against a silver/silver chloride(Ag/AgCl) reference electrode used as the target or output variable. The modeling results obtained show that the network with 4 neurons in the input layer, 10 neurons in the hidden layer and 1 neuron in the output layer had a high correlation coefficient (R-value) of 0.850602 for the test data, and a low mean square error (MSE) of 0.0261294. 9enCathodic ProtectionArtificial Neural NetworksSacrificial AnodesArticle