Modeling the corrosion initiation time of slag concrete using the artificial neural network - O.A. Hodhod; H.I. Ahmed (HBRC Journal, 2014)
This paper focuses on the Artificial Neural Network (ANN) as an alternative approach to simulate the corrosion initiation time of slag concrete obtained from the error function solution to Fick’s second law of diffusion. The adopted network architecture consists of four neurons in the input layer, which represents the values of concrete cover depth, apparent chloride diffusion coefficient, chloride threshold value and surface chloride concentration, and one neuron in the output layer, which represents the value of the corresponding corrosion initiation time. Back Propagation (BP) algorithm was employed for the ANN training in which a Tansig function was used as the nonlinear transfer function. The research results obtained from both ANN model and the error function solution to Fick’s second law of diffusion demonstrate that the corrosion initiation time of slag concrete increases with increasing both the concrete cover and the chloride threshold value and decreases with increasing both the surface chloride concentration and the chloride diffusion coefficient. Through the comparison of the estimated results from ANN model and the error function solution to Fick’s second law of diffusion, it was clear that there was a high correlation between the corrosion initiation time obtained from the error function solution to Fick’s second law of diffusion and the corresponding corrosion initiation time predicted by the ANN model.