Characterizing the Permeability of Kansas Concrete Mixes Used in PCC Pavements

Abstract

Reliable and economical design of portland cement concrete (PCC) pavement structural systems relies on various factors, among which is the proper characterization of the expected permeability response of the concrete mixes. Permeability is a highly important factor that strongly relates the durability of concrete structures and pavement systems to changing environmental conditions. One of the most common environmental attacks that causes the deterioration of concrete structures is the corrosion of reinforcing steel due to chloride penetration. On an annual basis, corrosion-related structural repairs typically cost millions of dollars. To properly characterize the permeability response of a PCC pavement structure, the Kansas DOT (KDOT) generally runs the rapid chloride permeability test (RCPT) to determine the resistance of concrete to penetration of chloride ions. RCPT typically measures the number of coulombs passing through a concrete sample over a period of 6 h at a concrete age of 7, 28, and 56 days. In this study, back-propagation artificial neural network (ANN) and regression-based permeability response prediction models for rapid chloride penetration test were developed by using the database provided by KDOT to reduce the duration of the testing period or ultimately eliminate the need to conduct the RCPT. The back-propagation ANN learning technique proved to be an efficient methodology to produce relatively accurate permeability response-prediction models. Comparison of the prediction accuracy of the developed models proved that ANN models have outperformed their counterpart regression-based models. The sensitivity analysis also was performed on randomly selected data sets to evaluate the reliability of developed models. The developed ANN models proved effective in characterizing the permeability (RCPT results) response of concrete mixes used in PCC pavements.