4.7 Article

Flexural fracture parameters of polypropylene fiber reinforced geopolymer

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DOI: 10.1016/j.jmrt.2023.03.035

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Polypropylene fiber; Fracture; Reinforcing index; Geopolymer; Toughness

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Geopolymer concrete can effectively reduce the negative environmental impacts of cement manufacturing. In this study, the effect of polypropylene fibers (PP fiber) with different properties on fracture toughness was evaluated. Experimental groups of PP fiber geopolymers were tested, and the results showed that the reinforcing index influenced the fracture toughness, with an initial increase and subsequent decrease. Low water-binder ratios generally led to higher fracture properties. PP fiber showed better toughness enhancement and economic benefits compared to other fibers.
Geopolymer concrete is an effective solution for widely limiting the negative environ-mental impacts of the cement manufacturing world. In this research, the polypropylene fibers (PP fiber) with various volume fractions, lengths, reinforcing index as well as water-binder ratios (WBR) were mixed into geopolymers to evaluate the effect on their FrT (fracture toughness). A total of 28 experimental groups of different polypropylene fiber geopolymers were considered. In the three-point bending fracture examination, the deformation and displacement fields of polypropylene fiber reinforced geopolymer (PPFRG) notched beams during the fracture process were observed through digital image correla-tion (DIC). The results show that, the fiber reinforcing index can be used to consider the influence of fiber length and fiber content. In increasing the reinforcing index, the FrT first increased, but after reaching the peak, it decreased. In terms of fracture properties, the PPFRG with a low WBR (0.35) is generally more significant than that of a high WBR (0.38). Comparing the PP fiber with the other fibers in literature, the more substantial toughness enhancement effect and better economy of PP fiber have been proved. Furthermore, this paper established a fracture toughness calculation model through regression analysis. (c) 2023 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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