The role of nanomaterials in geopolymer concrete composites: A state-of-the-art review

Ahmed, Hemn Unis; Mohammed, Azad A.; Mohammad, Ataei

doi:10.1016/j.jobe.2022.104062

Cited by 58 publications

(31 citation statements)

References 219 publications

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“…Geopolymers are novel cementitious materials that have the potential to completely replace conventional Portland cement composites while emitting less CO 2 [ 5 ]. The term ‘geopolymer’ refers to the binder product of an alkaline liquid reaction with silicon (Si) and aluminum (Al) in the source material [ 6 ]. Geopolymers are more cost-effective, sustainable, and resilient infrastructures because they are hard, stable at high temperatures, and cost-effective.…”

Section: Introductionmentioning

confidence: 99%

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar

et al. 2022

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In recent years, geopolymer has been developed as an alternative to Portland cement (PC) because of the significant carbon dioxide emissions produced by the cement manufacturing industry. A wide range of source binder materials has been used to prepare geopolymers; however, fly ash (FA) is the most used binder material for creating geopolymer concrete due to its low cost, wide availability, and increased potential for geopolymer preparation. In this paper, 247 experimental datasets were obtained from the literature to develop multiscale models to predict fly-ash-based geopolymer mortar compressive strength (CS). In the modeling process, thirteen different input model parameters were considered to estimate the CS of fly-ash-based geopolymer mortar. The collected data contained various mix proportions and different curing ages (1 to 28 days), as well as different curing temperatures. The CS of all types of cementitious composites, including geopolymer mortars, is one of the most important properties; thus, developing a credible model for forecasting CS has become a priority. Therefore, in this study, three different models, namely, linear regression (LR), multinominal logistic regression (MLR), and nonlinear regression (NLR) were developed to predict the CS of geopolymer mortar. The proposed models were then evaluated using different statistical assessments, including the coefficient of determination (R2), root mean squared error (RMSE), scatter index (SI), objective function value (OBJ), and mean absolute error (MAE). It was found that the NLR model performed better than the LR and MLR models. For the NLR model, R2, RMSE, SI, and OBJ were 0.933, 4.294 MPa, 0.138, 4.209, respectively. The SI value of NLR was 44 and 41% lower than the LR and MLR models’ SI values, respectively. From the sensitivity analysis result, the most effective parameters for predicting CS of geopolymer mortar were the SiO2 percentage of the FA and the alkaline liquid-to-binder ratio of the mixture.

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Section: Introductionmentioning

confidence: 99%

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar

et al. 2022

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“…The development of polycondensation between tetrahedral aluminosilicate gels might explain the improvement in CS. Chemical reactions take place in Al-Si materials in highly alkaline solution conditions, resulting in polymeric Si-O-Al-O bonds [ 26 , 32 ]. The alkali concentration has a crucial role in improving the polymerization process and strength growth.…”

Section: Analysis Of Compressive Strength (Cs)mentioning

confidence: 99%

“…The alkali content and Na 2 O/Al 2 O 3 ratio contribute more efficiently to the formation of the geopolymer phase. The pH increased as the molarity increased, which promotes the amorphous phase formation [ 3 , 26 , 32 ].…”

Section: Analysis Of Compressive Strength (Cs)mentioning

confidence: 99%

“…Between 2.5 and 3, there was just a little rise in CS value. The influence of temperature as a curing condition in the Si/Al ratio was examined for exposure durations of 24 to 48 h at 60, 75, and 90 °C [ 32 , 35 ]. They found that a maximum CS value was attained with a Si/Al ratio of 2.5 and a temperature of 75 °C for 24 h. The greatest CS value for 14M with the oven-cured specimen is 35.7 MPa after 56 days.…”

Section: Analysis Of Compressive Strength (Cs)mentioning

confidence: 99%

“…When an activator agent is added to binders, it promotes the Si and Al crystallization of the structure, which transforms the oligomer to monomer and accelerates the chemical process of geopolymerization in the matrix. When GGBS and FA were used as binders along with a superplasticizer, it enhanced the workability and the better development of strength compared with the mix without superplasticizer [ 14 , 31 , 32 , 37 ]. The influence of temperature as a curing condition in the A/B ratio was studied at 60, 75, and 90 °C for exposure durations ranging from 24 to 48 h [ 38 , 39 ].…”

Section: Analysis Of Compressive Strength (Cs)mentioning

confidence: 99%

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Assessment of Destructive and Nondestructive Analysis for GGBS Based Geopolymer Concrete and Its Statistical Analysis

et al. 2022

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Geopolymer is the alternative to current construction material trends. In this paper, an attempt is made to produce a sustainable construction composite material using geopolymer. Ground granulated blast furnace slag (GGBS)-based geopolymer concrete was prepared and tested for different alkaline to binder ratios (A/B). The effect of various temperatures on compressive strength properties was assessed. The cubes were exposed to temperature ranging from 50 to 70 °C for a duration ranging from 2 to 10 h, and the compressive strength of the specimens was analyzed for destructive and non-destructive analysis and tested for 7, 28, and 90 days. The obtained compressive strength (CS) results were analyzed employing the probability plot (PP) curve, distribution overview curve (DOC), probability density function (PDF), Weibull, survival, and hazard function curve. Maximum compressive strength was achieved for the temperature of 70 °C and an A/B of 0.45 for destructive tests and non-destructive tests with 44.6 MPa and 43.56 MPa, respectively, on 90 days of testing. The survival and hazard function curves showed incremental distribution characteristics for 28 and 90 days of testing results with a probability factor ranging from 0.8 to 1.0.

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Analysis and prediction of the effect of Nanosilica on the compressive strength of concrete with different mix proportions and specimen sizes using various numerical approaches

Ali

Muayad

Mohammed

2022

Structural Concrete

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Introducing nanotechnology in concrete is one of the most significant successes in enhancing the mechanical properties of concrete. It affects the quality of the microstructure of the concrete due to its extremely small nanoparticles leading to a faster reaction due to its large surface area. The nanoparticles fill the concrete's pores, resulting in considerably improved strengths in the early ages of the concrete. This research used six models; linear regression (LR), multilinear regression (MLR), nonlinear regression (NLR), pure quadratic (PQ), interaction (IA), and full quadratic (FQ), to predict the compressive strength of the concrete modified with different Nanosilica contents for various mix proportions. The models were applied to three datasets, with 420 collected from several research studies. The ranges of the concrete mix proportions used in this study were as follows, water/cement ratio (w/c) ranged between 0.1 and 1, cement content (C) ranged between 153.81 and 1200 kg/m3, fine aggregate content (FA) ranged between 492 and 2270 kg/m3, coarse aggregate content (CA) ranged between 617 and 2900 kg/m3, superplasticizer (SP) ranged between 0% and 6.7%, coarse aggregate size (CAS) ranged between 6 and 50.8 mm, fine aggregate size (FAS) ranged between 0.025 and 10 mm, Nanosilica content (NS%) ranged between 0% and 15%, w/c of 0.4–0.6 and the curing time (days) ranged between 3 and 180. The compressive strength of the collected datasets ranged from 3 to 120 MPa. The parameters that have improved the analyzed datasets of the compressive strength were the content of Nanosilica and the SP. The models were assessed for their accuracy using multiple assessment criteria; the most evident ones are; the objective function (OBJ), root mean square error (RMSE), Scatter Index (SI), and the maximum absolute error (MAE). The analytical models and the model comparison operations will be shown in detail in this research, indicating that the most effective and accurate model is the FQ model; where coefficient of determination (R2) of 0.96, RMSE of 3.49 MPa, MAE of 2.96 MPa, and SI of 0.08 were conducted from the analytical studies. From the developed models, the compressive strength of the concrete up to a high compressive strength level with high accurately can be predicted. Also, the compressive strength of the concrete can be accurately predicted for various sizes of aggregate and specimens.

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The role of nanomaterials in geopolymer concrete composites: A state-of-the-art review

Cited by 58 publications

References 219 publications

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar

Assessment of Destructive and Nondestructive Analysis for GGBS Based Geopolymer Concrete and Its Statistical Analysis

Analysis and prediction of the effect of Nanosilica on the compressive strength of concrete with different mix proportions and specimen sizes using various numerical approaches

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