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논문 기본 정보

자료유형
학술저널
저자정보
(School of Architecture and Civil Engineering, Xi'an University of Science and Technology) (School of Architecture and Civil Engineering, Xi'an University of Science and Technology) (School of Architecture and Civil Engineering, Xi'an University of Science and Technology) (School of Architecture and Civil Engineering, Xi'an University of Science and Technology) (School of Architecture and Civil Engineering, Xi'an University of Science and Technology)
저널정보
한국재료학회 한국재료학회지 한국재료학회지 제27권 제8호
발행연도
수록면
451 - 458 (8page)

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초록· 키워드

The service life of coal gangue concrete(CGC) strongly depends on the capillary water absorption, this water absorption is susceptible to freeze-thaw cycles. In this paper, the cumulative water absorption and sorptivity were obtained to study the effects of 0, 0.5, 1.0, and 1.5 % steel fiber volume fraction added on the water absorption of CGC. Sorptivity and freeze-thaw tests were conducted, and the capillary water absorption was evaluated by the rate of water absorption(sorptivity). Three prediction models for the initial sorptivity of steel fiber reinforced coal gangue concrete(SFRCGC) under freeze-thaw cycles were established to evaluate the capillary water absorption of SFRCGC. Results showed that, without freeze-thaw cycles, the water absorption of CGC decreased when steel fiber at 1.0 % volume fraction was added, however, the water absorption increased with the addition of 0.5 or 1.5 % steel fibers. Once the SFRCGC specimens were exposed to freeze-thaw cycles, the water absorption of SFRCGC significantly increased, and 1.0 % steel fiber in volume fraction added to CGC caused the lowest water absorption, except for the case of the sample without steel fibers added. The CGC with steel fiber at 1.0 % volume fraction performed better. The SFRCGC has a strong response to freeze-thaw cycles. Results also showed that the linear function prediction model is practical in the field of engineering because of its simple form and a relatively high precision. Although the polynomial prediction model presents the highest computation precision among the three models, the complicated form and too many coefficients make it impractical for engineering applications.
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