Journal of the Korea Concrete Institute (콘크리트학회논문집)

Korea Concrete Institute (한국콘크리트학회)
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Analysis Technique for Chloride Behavior Using Apparent Diffusion Coefficient of Chloride Ion from Neural Network Algorithm

신경망 이론을 이용한 염소이온 겉보기 확산계수 추정 및 이를 이용한 염화물 해석

  • 이학수 (한남대학교 건설시스템공학과) ;
  • 권성준 (한남대학교 건설시스템공학과)
  • Received : 2012.04.24
  • Accepted : 2012.06.18
  • Published : 2012.08.31
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Abstract

Evaluation of chloride penetration is very important, because induced chloride ion causes corrosion in embedded steel. Diffusion coefficient obtained from rapid chloride penetration test is currently used, however this method cannot provide a correct prediction of chloride content since it shows only ion migration velocity in electrical field. Apparent diffusion coefficient of chloride ion based on simple Fick's Law can provide a total chloride penetration magnitude to engineers. This study proposes an analysis technique to predict chloride penetration using apparent diffusion coefficient of chloride ion from neural network (NN) algorithm and time-dependent diffusion phenomena. For this work, thirty mix proportions with the related diffusion coefficients are studied. The components of mix proportions such as w/b ratio, unit content of cement, slag, fly ash, silica fume, and fine/coarse aggregate are selected as neurons, then learning for apparent diffusion coefficient is trained. Considering time-dependent diffusion coefficient based on Fick's Law, the technique for chloride penetration analysis is proposed. The applicability of the technique is verified through test results from short, long term submerged test, and field investigations. The proposed technique can be improved through NN learning-training based on the acquisition of various mix proportions and the related diffusion coefficients of chloride ion.

염화물 이온은 콘크리트 내부로 유입되어 철근부식을 야기하므로 염화물 침투 평가는 매우 중요하다. 전기영동실험을 통한 촉진확산계수가 현실적으로 많이 쓰이고 있지만, 이는 자유염화물 이온에 대한 전기장 내의 이온속도를 나타낼 뿐이므로 염화물량에 대한 명확한 해를 제공하지 못한다. 겉보기 확산계수는 단순한 Fick의 이론을 배경으로 엔지니어에게 전염화물의 확산을 명확하게 제공할 수가 있다. 이 연구는 인공신경망이론을 이용하여 최적의 확산계수를 도출하고 시간의존성 확산계수를 이용하여 염화물 침투를 평가할 수 있는 기법을 제시하는 것이다. 이를 위해 기존의 연구에서 30개의 배합 및 염소이온 겉보기 염화물 확산계수를 인용하였으며, 배합인자(물결합재비, 단위시멘트량, 슬래그, 플라이애쉬, 실리카퓸, 단위 잔골재 및 굵은 골재)를 뉴런으로 선택하여 확산계수에 대한 학습을 훈련하였다. 또한 시간의존성 확산계수를 고려하여 단순한 Fick 법칙으로 염화물 침투를 평가할 수 있는 기법을 제시하였다. 장기침지실험 및 실태조사 결과를 이용하여 제안된 기법의 결과와 비교를 수행하였으며, 그 적용성을 평가하였다. 이 기법은 다양한 배합 및 관련 확산계수의 입수 및 학습을 통하여 더욱 합리적인 기법으로 발전할 수 있다.

Keywords

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