@Article{cmc.2022.017262,
AUTHOR = {Hyun Kyu Shin, Ha Young Kim, Sang Hyo Lee},
TITLE = {Convolutional Neural Network-Based Regression for Predicting the Chloride Ion Diffusion Coefficient of Concrete},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {70},
YEAR = {2022},
NUMBER = {3},
PAGES = {5059--5071},
URL = {http://www.techscience.com/cmc/v70n3/44922},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2022.017262}
}