Characterizing the permeability of concrete mixes used in transportation applications: a neuronet approach

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 which strongly relates the durability of concrete structures and pavement systems to changing environmental conditions. One of the most common environmental attacks which cause 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. This durability problem has gotten widespread interest in recent years due to its incidence rate and the associated high repair costs. For this reason, material characterization is one of the best methods to reduce repair costs. To properly characterize the permeability response of PCC pavement structure, the Kansas Department of Transportation (KDOT) generally runs the Rapid Chloride Permeability test to determine the resistance of concrete to penetration of chloride ions as well as the Boil test to determine the percent voids in hardened concrete. Rapid Chloride test 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. Boil Test measures the volume of permeable pore space of the concrete sample over a period of five hours at a concrete age of 7, 28, and 56 days. In this research, backpropagation Artificial Neural Network (ANN)-based and Regression-based permeability response prediction models for Rapid Chloride and Boil tests are developed by using the databases provided by KDOT in order to reduce or eliminate the duration of the testing period. Moreover, another set of ANN- and Regression-based permeability prediction models, based on mix-design parameters, are developed using datasets obtained from the literature. The backpropagation ANN learning technique proved to be an efficient methodology to produce a relatively accurate permeability response prediction models. Comparison of the prediction accuracy of the developed ANN models and regression models proved that ANN models have outperformed their counterpart regression-based models. Overall, it can be inferred that the developed ANN-Based permeability prediction models are effective and applicable in characterizing the permeability response of concrete mixes used in transportation applications.