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Title |
The Prediction of Durability Performance for Chloride Ingress in Fly Ash Concrete by Artificial Neural Network Algorithm
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Authors |
권성준(Seung-Jun Kwon) ; 윤용식(Yong-Sik Yoon) |
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DOI |
https://doi.org/10.11112/jksmi.2022.26.5.127 |
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Keywords |
통과 전하량; 염해; 플라이애시; 게이트 순환 유닛; 딥-러닝 모델 Passed charge; Chloride ingress; Fly ash; GRU; Deep learning model |
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Abstract |
In this study, RCPTs (Rapid Chloride Penetration Test) were performed for fly ash concrete with curing age of 4 ~ 6 years. The concrete mixtures were prepared with 3 levels of water to binder ratio (0.37, 0.42, and 0.47) and 2 levels of substitution ratio of fly ash (0 and 30%), and the improved passed charges of chloride ion behavior was quantitatively analyzed. Additionally, the results were trained through the univariate time series models consisted of GRU (Gated Recurrent Unit) algorithm and those from the models were evaluated. As the result of the RCPT, fly ash concrete showed the reduced passed charges with period and an more improved resistance to chloride penetration than OPC concrete. At the final evaluation period (6 years), fly ash concrete showed 'Very low' grade in all W/B (water to binder) ratio, however OPC concrete showed 'Moderate' grade in the condition with the highest W/B ratio (0.47). The adopted algorithm of GRU for this study can analyze time series data and has the advantage like operation efficiency. The deep learning model with 4 hidden layers was designed, and it provided a reasonable prediction results of passed charge. The deep learning model from this study has a limitation of single consideration of a univariate time series characteristic, but it is in the developing process of providing various characteristics of concrete like strength and diffusion coefficient through additional studies.
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