Developing an artificial neural network model to evaluate chloride diffusivity in high performance concrete

Abstract

Chloride attack is one of the major causes of deterioration of reinforced concrete structures. In order to evaluate chloride behavior in concrete, a reasonable prediction for the diffusion coefficient of chloride ion (Dc), which governs the mechanism of chloride diffusion inside concrete, is basically required. However, it is difficult to obtain chloride diffusion coefficients from experiments due to time and cost limitations. This study focuses on the artificial neural network (ANN) as an alternative approach to evaluate the chloride diffusivity of high performance concrete (HPC). A total of 300 different data of fly ash (FA) and slag (GGBFS) concrete were collected from the literature. Two separate ANN models were developed for two types of HPC. The data used in the ANN model consisted of four input parameters which include W/B ratio, cement content, FA or GGBFS content and curing age. Output parameter is determined as diffusion coefficient of chloride ion. Back propagation (BP) algorithm was employed for the ANN training in which a Tansig function was used as the nonlinear transfer function. Through the comparison of the estimated results from ANN models and experimental data, it was clear that ANN models give high prediction accuracy. In addition, the research results demonstrate that using ANN models to predict chloride diffusion coefficient is practical and beneficial.