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.
Concrete as a construction material is widely used in nuclear vessel and plant for excellent radiation shielding. However the isolation characteristics in concrete may affect adversely in the case of fire and melt-down in nuclear vessel since temperature cooling down is very difficult from outside. This study is for development of high thermal conductive concrete, and its mechanical and thermal properties are evaluated. Magnetite aggregates with volume ratio of 42.3% (maximum) and steel powder of 1.5% are replaced with normal aggregates and thermal properties are evaluated. Thermal conductivity little increases by 30% addition of magnetite but rapidly increases afterwards. Finally thermal conductivity is magnified to 2.5 times in the case of 42.3% addition of magnetite. Steel powder has a positive effect on high thermal conduction to 106∼113%. Several models for thermal conduction like ACI, DEMM, and MEM are compared with test results and they are verified to reasonably predict the thermal conductivity with increasing addition of magnetite aggregates and steel powder.
Deterioration is accelerated due to additional intrusion of chloride ion in crack width in cracked concrete. In this paper, modeling on equivalent diffusion coefficient in cracked concrete is performed for 1-D (Anisotropic) and 2-D (Isotropic) diffusion based on steady state condition. In the previous research, rectangular shape of crack was considered but the shape was modified to wedge shape with torturity. For verification of the proposed model, crack is induced in concrete sample and migration test in steady state is performed for 1-D diffusion. For 2-D diffusion, previous test results are adopted for verification. Through considering wedge shape of crack with torturity, diffusion coefficients in 1-D and 2-D diffusion are reduced, and the more reasonable prediction is obtained. The results from the proposed model with torturity of 0.10~0.15 are shown to be in the best agreement with the test results.
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