Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm

This study attempted to predict corrosion current density in concrete using artificial neural networks (ANN) combined with imperialist competitive algorithm (ICA) used to optimize weights of ANN. For that reason, temperature, AC resistivity over the steel bar, AC resistivity remote from the steel bar, and the DC resistivity over the steel bar are considered as input parameters and corrosion current density as output parameter. The ICA–ANN model has been compared with the genetic algorithm to evaluate its accuracy in three phases of training, testing, and prediction. The results showed that the ICA–ANN model enjoys more ability, flexibility, and accuracy.

Steel reinforced concrete , Resistivity , Polarization , Imperialist competitive algorithm , Genetic algorithm , Artificial neural networks