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Mechanical properties and microscopic characteristics of steel fiber coal gangue concrete

  • Jiuyang Li
  • , Li Chen*
  • , Jingwei Luo
  • , Wenzhong Zhu
  • , Xinmei Fan
  • , Yuepeng Zhu
  • , Zicheng Zhang
  • *Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    48 Downloads (Pure)

    Abstract

    Incorporation of coal gangue in the concrete mixes can realize utilization of the solid waste and reduce extraction/use of natural aggregates. To improve the mechanical properties of coal gangue concrete, this paper studies use of steel fibre together with coal gangue coarse aggregate, coal gangue fine aggregate/sand in various concrete mixes. The effect of volume dosages of steel fibre and different levels of replacing nature coarse aggregate and river sand with coal gangue aggregates on concrete compressive strength was first investigated. Then, a design of experiment using orthogonal test was adopted to study concrete mixes with 3 factors, namely, coal gangue coarse aggregate, coal gangue sand and steel fibre, and each at 3 levels. Through multidimensional statistical data analysis of the test results, the primary and secondary factors and the optimal composition of the steel fibre reinforced coal gangue concrete were identified, and a grey prediction model for compressive strength of the concrete mixes established. The microstructural characteristics and failure mechanism of steel fiber reinforced coal gangue concrete was also studied and discussed.
    Original languageEnglish
    Article number1211129
    Number of pages16
    JournalFrontiers in Materials
    Volume10
    DOIs
    Publication statusPublished - 24 Jul 2023

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 12 - Responsible Consumption and Production
      SDG 12 Responsible Consumption and Production

    Keywords

    • steel fiber
    • coal gangue
    • compressive strength
    • orthogonal test
    • microscopic mechanism
    • grey prediction model

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