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Analysis of Chloride Penetration in Concrete with Mineral Admixtures using Neural Network Algorithm and Micro Modelling
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신경망 이론과 마이크로 모델링을 통한 혼화재를 사용한 콘크리트의 염화물 침투해석

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Type
Academic journal
Author
Kwon Seung-Jun (비앤티엔지니어링) Song Ha-Won (연세대학교) Byun Keun Joo (연세대학교) Park Chan Kyu (삼성물산)
Journal
KOREAN SOCIETY OF CIVIL ENGINEERS JOURNAL OF THE KOREAN SOCIETY OF CIVIL ENGINEERS A Vol.27 No.1A KCI Accredited Journals
Published
2007.1
Pages
117 - 129 (13page)

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Analysis of Chloride Penetration in Concrete with Mineral Admixtures using Neural Network Algorithm and Micro Modelling
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Chloride attack is one of the major deteriorations in concrete structures. In order to quantify chloride behavior in concrete, accurate prediction of ditfusivity coefficient of chloride ion is necessary. In conventional prediction equations, the apparent diffusivity coefficient is expressed only with the water to cement ratio (W/C), i.e., the detailed mix proportions of concrete are not considered to obtain the diffusivity coefficient of concrete with mineral admixtures. In this study, a numerical technique for chloride penetration in the concrete with mineral admixtures using diffusivity coefficient from neural network algorithm is proposed. In order to obtain the comparable data set considering various mineral admixtures such as ground granulated blast-furnace slag (GGBFS), fly ash (FA) and slica fume (SF), a series of rapid chloride penetration tests are performed and a limited number of diffusivity coefficients are obtained. Total eight neurons (seven material components in trial mixture designs and curing period in submerged condition) are used for neural network. Through the neural network, 120 diffusivity coefficients from 30 mixture designs are obtained and the average of analyzed data is evaluated to be about 7%, which can be statistically acceptable. Finally, an evaluation technique for chloride penetration in the HPC is also developed by utilizing multi component hydration heat model (MCHHM) and micro pore structure formation model (MPSFM) along with obtained diffusivity coefficients from neural network. The applicability of the developed technique is also verified by comparing the simulation results with experimental results as well as other data.
#chloride penetration #diffusivity coefficient #neural network #mineral admixtures #염화물 침투 #확산계수 #신경망 이론 #혼화재료

Contents

Abstract
요지
1. 서론
2. 인공신경망을 이용한 확산계수 추정
3. 염화물 침투 해석의 개요
4. 실험결과와의 비교 분석
5. 결론
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