Abstract
The durability performance of reinforced concrete (RC) building structures is significantly affected by the corrosion of the steel reinforcement due to chloride penetration, thus, the chloride ion diffusion coefficient should be investigated through experiments or theoretical equations to assess the durability of an RC structure. This study aims to predict the chloride ion diffusion coefficient of concrete, a heterogeneous material. A convolutional neural network (CNN)-based regression model that learns the condition of the concrete surface through deep learning, is developed to efficiently obtain the chloride ion diffusion coefficient. For the model implementation to determine the chloride ion diffusion coefficient, concrete mixes with w/c ratios of 0.33, 0.40, 0.46, 0.50, 0.62, and 0.68, are cured for 28 days; subsequently, the surface image data of the specimens are collected. Finally, the proposed model predicts the chloride ion diffusion coefficient using the concrete surface image data and exhibits an error of approximately 1.5E−12 /s. The results suggest the applicability of proposed model to the field of facility maintenance for estimating the chloride ion diffusion coefficient of concrete using images.
Highlights
In the construction industry, the demand for reinforced concrete (RC) structures with high durability, is increasing [1,2,3]
The conjecture of this study is that a deep convolution neural network (DCNN) that can learn data-based features by itself will be able to learn features in concrete images that may facilitate predicting the chloride ion diffusion coefficient
An experiment was performed by constructing a single DCNN for the chloride ion diffusion coefficient, using the image data
Summary
The demand for reinforced concrete (RC) structures with high durability, is increasing [1,2,3]. The service life of porous concrete materials that constitute RC structures, is adversely affected by chlorides that penetrate into the concrete from the outer surface [4,5]. Given that chloride ions have a direct impact on the durability performance of RC, they are used as an important indicator for durability analysis, design, and service life assessment [5,6]. Because chloride ion penetration is affected by various factors, such as the concrete characteristics and exposed environment, various studies have been conducted on the factors that affect chloride ion penetration [7]. It has been confirmed that dilution, tortuosity, and interfacial transition zone (ITZ) affect
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