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논문 기본 정보

자료유형
학술저널
저자정보
Jong-Han Lee (POSCO Engineering & Construction) Jong-Jae Lee (Sejong University) Baik-Soon Cho (Inje University)
저널정보
한국콘크리트학회 International Journal of Concrete Structures and Materials International Journal of Concrete Structures and Materials Vol.6 No.3
발행연도
2012.9
수록면
177 - 186 (10page)

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초록· 키워드

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The temperature distributions of concrete structures strongly depend on the value of thermal conductivity of concrete. However, the thermal conductivity of concrete varies according to the composition of the constituents and the temperature and moisture conditions of concrete, which cause difficulty in accurately predicting the thermal conductivity value in concrete. For this reason, in this study, back-propagation neural network models on the basis of experimental values carried out by previous researchers have been utilized to effectively account for the influence of these variables. The neural networks were trained by 124 data sets with eleven parameters: nine concrete composition parameters (the ratio of water?cement, the percentage of fine and coarse aggregate, and the unit weight of water, cement, fine aggregate, coarse aggregate, fly ash and silica fume) and two concrete state parameters (the temperature and water content of concrete). Finally, the trained neural network models were evaluated by applying to other 28 measured values not included in the training of the neural networks. The result indicated that the proposed method using a back-propagation neural algorithm was effective at predicting the thermal conductivity of concrete.

목차

Abstract
1. Introduction
2. Construction of Neural Network
3. Comparison of Estimated and MeasuredThermal Conductivity of Concrete
4. Conclusions
References

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