Evaluation Technique of Chloride Penetration Using Apparent Diffusion Coefficient and Neural Network Algorithm

Diffusion coefficient from chloride migration test is currently used; however this cannot provide a conventional solution like total chloride contents since it depicts only ion migration velocity in electrical field. This paper proposes a simple analysis technique for chloride behavior using apparent diffusion coefficient from neural network algorithm with time-dependent diffusion phenomena. For this work, thirty mix proportions of high performance concrete are prepared and their diffusion coefficients are obtained after long term-NaCl submerged test. Considering time-dependent diffusion coefficient based on Fick’s 2nd Law and NNA (neural network algorithm), analysis technique for chloride penetration is proposed. The applicability of the proposed technique is verified through the results from accelerated test, long term submerged test, and field investigation results.

[1]  Seung-Jun Kwon,et al.  Analysis technique for chloride penetration in cracked concrete using equivalent diffusion and permeation , 2012 .

[2]  Seung-Jun Kwon,et al.  Modeling of water permeability in early aged concrete with cracks based on micro pore structure , 2012 .

[3]  P. Bowen,et al.  Changes in portlandite morphology with solvent composition: Atomistic simulations and experiment , 2011 .

[4]  Seung-Jun Kwon,et al.  Evaluation of Chloride Penetration in High Performance Concrete Using Neural Network Algorithm and Micro Pore Structure , 2009 .

[5]  Koichi Maekawa,et al.  Multi-scale modeling of structural concrete , 2008 .

[6]  Keun-Joo Byun,et al.  An estimation of the diffusivity of silica fume concrete , 2007 .

[7]  Seung-Jun Kwon,et al.  Vorhersage der Lebensdauer von Betontragwerken im Seeklima unter Berücksichtigung gekoppelter zerstörender Vorgänge / Service Life Prediction of Concrete Structures under Marine Environment Considering Coupled Deterioration , 2006 .

[8]  Takafumi Noguchi,et al.  Modeling of hydration reactions using neural networks to predict the average properties of cement paste , 2005 .

[9]  E. Vesikari,et al.  Durability Design of Concrete Structures , 2004 .

[10]  Julia A. Stegemann,et al.  Prediction of unconfined compressive strength of cement paste with pure metal compound additions , 2002 .

[11]  Wang Ji-Zong,et al.  The application of automatic acquisition of knowledge to mix design of concrete , 1999 .

[12]  Michael D. A. Thomas,et al.  Modelling chloride diffusion in concrete: Effect of fly ash and slag , 1999 .

[13]  I-Cheng Yeh,et al.  Modeling of strength of high-performance concrete using artificial neural networks , 1998 .

[14]  J. Broomfield.  Corrosion of Steel in Concrete: Understanding, investigation and repair , 1996 .

[15]  Luping Tang,et al.  ELECTRICALLY ACCELERATED METHODS FOR DETERMINING CHLORIDE DIFFUSIVITY IN CONCRETE - CURRENT DEVELOPMENT , 1996 .

[16]  Safwan A. Khedr,et al.  Characteristics of Silica‐Fume Concrete , 1994 .

[17]  Nick R. Buenfeld,et al.  Factors influencing chloride-binding in concrete , 1990 .

[18]  A. Neville.  Properties of Concrete , 1968 .

[19]  Ha-Won Song,et al.  Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modeling , 2010 .

[20]  Ung Jin Na,et al.  Service life prediction of concrete wharves with early-aged crack: Probabilistic approach for chloride diffusion , 2009 .

[21]  Seung-Jun Kwon,et al.  A Study on Analytical Technique of Chloride Diffusion considering Characteristics of Mixture Design for High Performance Concrete using Mineral Admixture , 2005 .

[22]  K. Maekawa,et al.  Multi-scale Modeling of Concrete Performance , 2003 .

[23]  A C Smith,et al.  Corrosion of steel in concrete , 1982 .