• Title/Summary/Keyword: Fly ash component

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The Fundamental Characteristics for Mix Proportion of Multi-Component Cement (배합비에 따른 다성분계 시멘트의 기초특성)

  • Kim, Tae-Wan;Jeon, Jae-Woo;Seo, Min-A;Jo, Hyeon-Hyeong;Bae, Su-Yeon
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.20 no.3
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    • pp.66-74
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    • 2016
  • The aim of this research work is to investigate the mix proportion of multi-component cement incorporating ground granulated blast furnace(GGBFS), fly ash(FA) and silica fume(SF) as an addition to cement in ternary and quaternary combinations. The water-binder ratio was 0.45. In this study, 50% and 60% replacement ratios of mineral admixture to OPC was used, while series of combination of 20~40% GGBFS, 5~35% FA and 0~15% SF binder were used for fundamental characteristics tests. This study concern the GGBFS/FA ratio and SF contents of multi-component cement including the compressive strength, water absorptions, ultrasonic pulse velocity(UPV), drying shrinkage and X-ray diffraction(XRD) analysises. The results show that the addition of SF can reduce the water absorption and increase the compressive strength, UPV and drying shrinkage. These developments in the compressive strength, UPV and water absorption can be attributed to the fact that increase in the SF content tends basically to consume the calcium hydroxide crystals released from the hydration process leading to the formation of further CSH(calcium silicate hydrate). The strength, water absorption and UPV increases with an increase in GGBFS/FA ratios for a each SF contents. The relationship between GGBFS/FA ratios and compressive strength, water absorption, UPV is close to linear. It was found that the GGBFS/FA ratio and SF contents is the key factor governing the fundamental properties of multi-component cement.

Optimal Mix Design of High-Performance, Low-Heat Self-Compacting Concrete (고성능 저발열 자기충전 콘크리트의 최적 배합설계)

  • Kim, Young-Bong;Lee, Jun-Hae;Park, Dong-Cheon
    • Journal of the Korea Institute of Building Construction
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    • v.22 no.4
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    • pp.337-345
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    • 2022
  • The foundation of high-rise concrete building in coastal areas generally must be installed in an integrated manner, not separately, in order to prevent defects caused by stress on the upper and lower parts of the mounting surface and to manage the process smoothly. However, when performing integrated punching, there is a concern that temperature stress cracks may occur due to hydration heat. Due to the large member size, it is difficult to make a sufficient commitment, so it is necessary to mix concrete with high self-charging properties to ensure workability. In this research, the amount of high-performance spray and admixture used was adjusted as experimental variables to satisfy this required performance. Through the analysis of the results for each blending variable, it was found that the unit quantity was 155kg/m3 and the cement ratio in the binder was 18%, and the target values of the pre-concrete properties and compressive strength were satisfied. A four-component binder(18% cement, 50% slag fine powder, 27% fly ash, 5% silica fume) was used.

Application of Artificial Neural Networks for Prediction of the Flow and Strength of Controlled Low Strength Material (CLSM의 플로우 및 일축압축강도 예측을 위한 인공신경망 적용)

  • Lim, Jong-Goo;Kim, Yeon-Joong;Chun, Byung-Sik
    • Journal of the Korean Geotechnical Society
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    • v.27 no.1
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    • pp.17-24
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    • 2011
  • The characteristics of flow and strength of CLSM depend on the combination ratio including the fly ash, pond ash, cement, water quantity and etc. However, it is very difficult to draw the mechanism about the flow, strength and the mixing ratio of each components. Therefore, the method of calculation drawing the flow about the component ratio of CLSM and compression strength value is needed for the valid practical use of CLSM. To verify the efficiency of artificial neural network, new data which were not used for establishing the model were predicted and compared with the results of laboratory tests. In this research, it was used to evaluate the learning efficiency of the artificial neural network model and the prediction ability by changing the node number of hidden layer, learning rate, momentum, target system error and hidden layer. By using the results, the optimized artificial neural network model which is suitable for a flow and compressive strength estimate of CLSM was determined.