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.
Faculty of Civil Engineering, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 Wrocław, Poland.
Lukasz
L
Sadowski
SAMA Technical and Vocational Training College, Islamic Azad University, Ahvaz Branch, Ahvaz, Iran.
Mehdi
M
Nikoo
Artificial neural networks; Genetic algorithm; Imperialist competitive algorithm; Polarization; Resistivity; Steel reinforced concrete