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Preparation, characterization, and evaluation of compressive strength of polypropylene fiber reinforced geopolymer mortars.

ABSTRACT: The study of the fiber-matrix interface represents a crucial topic to determine the mechanical performance of geopolymer-based materials reinforced with polypropylene fibers (PPF). This research proposes the use of natural zeolite in the preparation geopolymers mortars through alkaline activation with NaOH, Ca(OH)₂ and Na₂SiO₃, and with river sand as a fine aggregate. PPF were incorporated into the geopolymer-based mortar matrix in different proportions like 0, 0.5, and 1 wt.%. The mortars were cured for 24 h at 60 °C and then aged for six days more at room temperature. All samples analyzed through compressive strength were also characterized by X-ray diffraction, thermal analysis, Infrared Spectroscopy, and scanning electron microscopy techniques. The results indicated that the best mix design among the ones used: NaOH (10 M), Na₂SiO₃/NaOH = 3, Ca(OH)₂ = 1.5 wt.% and PPF = 0.5 wt.%. The optimum mix design showed a compressive strength of 4.63 MPa on average. Besides, the fibers enhanced the compressive strength of those samples which the PP fibers probably have better dispersion inside the matrix of the geopolymer mortar.

SUBMITTER: Baykara H

PROVIDER: S-EPMC7168747 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Preparation, characterization, and evaluation of compressive strength of polypropylene fiber reinforced geopolymer mortars.

Baykara Haci H Cornejo Mauricio H MH Espinoza Andrés A García Enriqueta E Ulloa Nestor N

Heliyon 20200417 4

The study of the fiber-matrix interface represents a crucial topic to determine the mechanical performance of geopolymer-based materials reinforced with polypropylene fibers (PPF). This research proposes the use of natural zeolite in the preparation geopolymers mortars through alkaline activation with NaOH, Ca(OH)2 and Na2SiO3, and with river sand as a fine aggregate. PPF were incorporated into the geopolymer-based mortar matrix in different proportions like 0, 0 ...[more]

PMID: 32322731

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Project description:Accurate analysis of the strength of steel-fiber-reinforced concrete (SFRC) is important for ensuring construction quality and safety. Cube compression and splitting tensile tests of steel fiber with different varieties, lengths, and dosages were performed, and the effects of different varieties, lengths, and dosages on the compressive and splitting properties of secondary concrete were obtained. It was determined that the compression and splitting strengths of concrete could be effectively improved by the addition of end-hooked and milled steel fibers. The compressive and splitting strengths of concrete can be enhanced by increasing the fiber length and content. However, concrete also exhibits obvious uncertainty owing to the comprehensive influence of steel fiber variety, fiber length, and fiber content. In order to solve this engineering uncertainty, the traditional RBF neural network is improved by using central value and weight learning strategy especially. On this basis, the RBF fuzzy neural network prediction model of the strength of secondary steel fiber-reinforced concrete was innovatively established with the type, length and content of steel fiber as input information and the compressive strength and splitting tensile strength as output information. In order to further verify the engineering reliability of the prediction model, the compressive strength and splitting tensile strength of steel fiber reinforced concrete with rock anchor beams are predicted by the prediction model. The results show that the convergence rate of the prediction model is increased by 15%, and the error between the predicted value and the measured value is less than 10%, which is more efficient and accurate than the traditional one. Additionally, the improved model algorithm is efficient and reasonable, providing technical support for the safe construction of large-volume steel fiber concrete projects, such as rock anchor beams. The fuzzy random method can also be applied to similar engineering fields.

Dataset Information

Preparation, characterization, and evaluation of compressive strength of polypropylene fiber reinforced geopolymer mortars.

Publications

Preparation, characterization, and evaluation of compressive strength of polypropylene fiber reinforced geopolymer mortars.

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