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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:Geopolymer concrete is an inorganic concrete that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to the geopolymer concrete being an eco-efficient and environmentally friendly construction material. A variety of ashes used as the binder in geopolymer concrete such as fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin ash, and Palm oil fuel ash, fly ash was commonly consumed to prepare geopolymer concrete composites. The most important mechanical property for all types of concrete composites, including geopolymer concrete, is the compressive strength. However, in the structural design and construction field, the compressive strength of the concrete at 28 days is essential. Therefore, achieving an authoritative model for predicting the compressive strength of geopolymer concrete is necessary regarding saving time, energy, and cost-effectiveness. It gives guidance regarding scheduling the construction process and removal of formworks. In this study, Linear (LR), Non-Linear (NLR), and Multi-logistic (MLR) regression models were used to develop the predictive models for estimating the compressive strength of fly ash-based geopolymer concrete (FA-GPC). In this regard, a comprehensive dataset consists of 510 samples were collected in several academic research studies and analyzed to develop the models. In the modeling process, for the first time, twelve effective variable parameters on the compressive strength of the FA-GPC, including SiO2/Al2O3 (Si/Al) of fly ash binder, alkaline liquid to binder ratio (l/b), fly ash (FA) content, fine aggregate (F) content, coarse aggregate (C) content, sodium hydroxide (SH)content, sodium silicate (SS) content, (SS/SH), molarity (M), curing temperature (T), curing duration inside ovens (CD) and specimen ages (A) were considered as the modeling input parameters. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2) were used to evaluate the efficiency of the developed models. The results indicated that the NLR model performed better for predicting the compressive strength of FA-GPC mixtures compared to the other models. Moreover, the sensitivity analysis demonstrated that the curing temperature, alkaline liquid to binder ratio, and sodium silicate content are the most affecting parameter for estimating the compressive strength of the FA-GPC.

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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