Development of a mix-design based Rapid Chloride Permeability assessment model using neuronets

Corrosion of reinforcing steel due to chloride penetration is one of the most common causes of deterioration in concrete pavement structures. On an annual basis, millions of dollars are spent on corrosion-related repairs. High incidence rates and repair costs have stimulated widespread research interests in order to properly assess the durability problem of concrete pavements. Chloride penetration of concrete pavement structures is determined through the Rapid Chloride Permeability test (RCPT), which typically measures the number of coulombs passing through a concrete sample over a period of six hours at a concrete age of 7, 28, and 56 days. In a composite material, such as concrete, the parameters of the mixture design and interaction between them determine the behavior of the material. Previous studies have shown that Artificial Neural Network (ANN) based material modeling approach has been successfully used to capture complex interactions among input and output variables. In this study, back-propagation ANN, and Regression-based permeability response prediction models were developed to assess the permeability potential of various concrete mixes using data obtained from actual Rapid Chloride Permeability tests. The back-propagation ANN learning technique proved to be an efficient method to produce relatively accurate permeability response prediction models. Comparison of the prediction accuracy of the developed ANN models and the regression model proved that the developed ANN model outperformed the regression-based model. The developed ANN models have high predictive capability to properly assess the chloride permeability of concrete mixes based on various mix-design parameters. These models can reliably be used for permeability prediction tasks in order to reduce or eliminate the duration of the testing as well as the sample preparation periods required for proper RCP testing.