Convolutional Neural Network-Based Regression for Predicting the Chloride Ion Diffusion Coefficient of Concrete
- Title
- Convolutional Neural Network-Based Regression for Predicting the Chloride Ion Diffusion Coefficient of Concrete
- Author
- 신현규
- Keywords
- Chloride ion diffusion coefficient; convolutional neural network; deep learning
- Issue Date
- 2021-10
- Publisher
- TECH SCIENCE PRESS
- Citation
- CMC-COMPUTERS MATERIALS & CONTINUA; 2022, 70 3, p5059-p5071, 13p.
- 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 m2/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.
- URI
- https://www.proquest.com/docview/2590734351?accountid=11283https://repository.hanyang.ac.kr/handle/20.500.11754/170333
- ISSN
- 15462218; 15462226
- DOI
- 10.32604/cmc.2022.017262
- Appears in Collections:
- ETC[S] > 연구정보
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