Journal of Advanced Concrete Technology
Online ISSN : 1347-3913
ISSN-L : 1346-8014
Volume 23, Issue 7
Displaying 1-2 of 2 articles from this issue
Scientific paper
  • Masahito Shibata, Kazuko Haga, Hitoshi Owada, Tsuneki Ichikawa
    2025Volume 23Issue 7 Pages 285-295
    Published: July 19, 2025
    Released on J-STAGE: July 19, 2025
    JOURNAL OPEN ACCESS

    For elucidating the validity of accelerated Ca leaching tests, the chemical and physical changes of a hardened ordinary Portland cement induced by the leaching into deionized water were compared with those induced by the accelerate leachings with proton-type cation exchange resin for the continuous elimination of Ca2+, with an electrostatic electric field for the electrophoretic elimination of Ca2+, and with NH4NO3 solutions for the elimination of OH. The use of the resin and the electrostatic field were found to be inappropriate because both the methods caused the formation of strong acid which severely attacked and etched the paste. The use of a NH4NO3 solution was an appropriate method for the accelerated test, since Ca2+ was homogeneously leached in the paste and the amount of the leaching was controllable by properly selecting the concentration and the volume of the NH4NO3 solution.

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  • Zongcai Deng, Dongyue Liu
    2025Volume 23Issue 7 Pages 296-309
    Published: July 25, 2025
    Released on J-STAGE: July 25, 2025
    JOURNAL OPEN ACCESS

    To develop ECC with excellent impact resistance, this study investigates the impact performance of hybrid fiber reinforced specimens through drop-weight impact tests. The results show that the number of impacts to failure increases with fiber volume and fiber modulus. At the same volume of fibers, PE fiber reinforced specimens exhibit impact resistance 3.45 times and 2.92 times higher than those reinforced with PP fibers and glass fibers, respectively. The specimens reinforced with high modulus steel fibers exhibit localized punching shear damage at the impact site while maintaining better structural integrity. An impact damage evolution equation and a service life prediction model are established based on the two-parameter Weibull distribution. A comparative analysis of various algorithmic models is conducted to advance the application of machine learning in predicting the failure impact energy of the specimens.

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