An Effect of NaOH Molarity on Fly Ash—Metakaolin-Based Self-Compacting Geopolymer Concrete

Arun, Byravan; Nagaraja, P.; Srishaila, J M

doi:10.1007/978-981-13-3317-0_21

Cited by 19 publications

(17 citation statements)

References 4 publications

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“…For the metakaolin-based GPC mix subjected to normal air curing, the ratio of 7 days to 28 days compressive strength was found to be 73% and 88% [ 28 ]. Tests on self-compacting GPC based on fly ash and metakaolin show that the 7 days compressive strength value is close to the 28 days strength [ 58 ].…”

Section: State-of-the-art Review Of Mechanical Properties Of Geopolymer Concretementioning

confidence: 99%

Survey of Mechanical Properties of Geopolymer Concrete: A Comprehensive Review and Data Analysis

2021

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Mechanical properties and data analysis for the prediction of different mechanical properties of geopolymer concrete (GPC) were investigated. A relatively large amount of test data from 126 past works was collected, analyzed, and correlation between different mechanical properties and compressive strength was investigated. Equations were proposed for the properties of splitting tensile strength, flexural strength, modulus of elasticity, Poisson’s ratio, and strain corresponding to peak compressive strength. The proposed equations were found accurate and can be used to prepare a state-of-art report on GPC. Based on data analysis, it was found that there is a chance to apply some past proposed equations for predicting different mechanical properties. CEB-FIP equations for the prediction of splitting tensile strength and strain corresponding to peak compressive stress were found to be accurate, while ACI 318 equations for splitting tensile and elastic modulus overestimates test data for GPC of low compressive strength.

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Section: State-of-the-art Review Of Mechanical Properties Of Geopolymer Concretementioning

confidence: 99%

Survey of Mechanical Properties of Geopolymer Concrete: A Comprehensive Review and Data Analysis

2021

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“…Arun B.R. et al [6] investigated that by increasing the percentage replacement of GGBFS to FA in SCGC enhanced the mechanical strength. Yamini J. et al [7] discovered that the optimal amount of RHA replacement was 5% in their investigation on the mechanical characteristics of SCGC mixed with GGBFS and RHA.…”

Section: Constituents Of Scgc Ggbfsmentioning

confidence: 99%

“…B.R. Arun et al [6] carried out studies in which they varied the proportion of FA with MK. They concluded that when the percentage of MK raised from 0 to 30%, the compressive strength decreased regardless of molarity.…”

Section: Box Testmentioning

confidence: 99%

Self-compacting geopolymer concrete

Amala¹,

Harishmita²

2022

ijhs

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Self-compacting geopolymer concrete (SCGC) is a novel material. and a modern high-performance concrete that doesn’t require Ordinary Portland cement and additional compaction. SCGC is made up of industrial byproducts, with high Alumina and Silica composition, and uses super plasticizer as a binder for matrix formation and strength. This research examines SCGC, which is prepared with various percentages of Ground Granulated Blast Furnace Slag (GGBFS), Silica Fume (SF) and Metakaolin (MK), activated with various molarities of alkaline solution containing sodium hydroxide (NaOH) and sodium silicate (Na2SiO3). Fresh and hardened characteristics along with microstructural studies are discussed. The beams are analysed using ANSYS software.

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“…The details of the data used for training the ANN model are adopted from previous research papers published in various international journals 4–15,17–22,27,30–33,35,71,91–127 are shown in Table 1. Table 2 shows the data used for testing the model.…”

Section: Training and Testing Datamentioning

confidence: 99%

“…The strength of the geopolymer concrete (GPC) depends on various factors such as type and amount of fly ash, 4 additives, 5–10 alkalinity ratio (i.e., sodium hydroxide to sodium silicate ratio [SH/SS]), 6,11–13 the concentration of NaOH, 5,13–17 the ratio of alkaline activator to the binder, 14,18 and size and amount of aggregates 15 . In addition to this, the strength of GPC also depends on the rest period before curing of concrete, 17 curing time, and curing temperature 12,15,17,19–22 at which GPC is cured; also amount of superplasticizer and extra water added to enhance other properties such as workability of GPC.…”

Section: Introductionmentioning

confidence: 99%

Prediction of compressive strength of geopolymer concrete using machine learning techniques

Gupta

Rao

2021

Structural Concrete

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Geopolymer concrete (GPC) is the result of an inorganic polymerization reaction that takes place in presence of an alkaline medium in the materials such as fly ash and slag, which are rich in silicates and aluminates. In this study, an artificial neural network (ANN), multiple linear regression, and the multivariate nonlinear regression (MNLR) models were designed to predict the 28 days compressive strength of the GPC. To train the models, a total of 289 data sets were used, which were published by different researchers in the open literature. The input parameters, namely, binder content, amount of slag, rest period, curing temperature, curing period, the ratio of NaOH/Na2SiO3, amount of superplasticizer, extra water added, the molarity of NaOH, alkaline activator to binder ratio, amount of coarse, and fine aggregate were used. The performance of the ANN model was better than the multi linear regression (MLR) and multi non‐linear regression (MNLR) models to estimate the strength. The sensitivity analysis on ANN was also performed to study the influence of each parameter. The sensitivity analysis shows that the ratio of NaOH/Na2SiO3 significantly affects the compressive strength of GPC.

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An Effect of NaOH Molarity on Fly Ash—Metakaolin-Based Self-Compacting Geopolymer Concrete

Cited by 19 publications

References 4 publications

Survey of Mechanical Properties of Geopolymer Concrete: A Comprehensive Review and Data Analysis

Survey of Mechanical Properties of Geopolymer Concrete: A Comprehensive Review and Data Analysis

Self-compacting geopolymer concrete

Prediction of compressive strength of geopolymer concrete using machine learning techniques

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