Geopolymer Concrete: A Review

Ramesh, Gomasa

doi:10.35940/ijse.a1302.111221

“…Recent research indicated that CO2 levels reached a record high of 417.1 parts per million, which is 2.4 parts per million more than in the previous year. This number raises an alarm since rising CO2 levels affect the environment by causing a rise in atmospheric pressure and temperature (G. Ramesh, 2021). This increase may have an impact on the CO2 adsorption on amorphous silica surfaces and may cause materials that are now amorphous to transition into crystallization phases.…”

Section: Introductionmentioning

confidence: 99%

New random intelligent chemometric techniques for sustainable geopolymer concrete: low-energy and carbon-footprint initiatives

Jibril,

Malami,

Jibrin

et al. 2023

Asian J Civ Eng

6

0

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The construction industry, being a significant contributor to greenhouse gas emissions, facing considerable attention and demand on account of the increasing global apprehension regarding climate change and its adverse impacts on environments. Geopolymer shows itself as a viable and sustainable alternative to the Portland cement binder in civil infrastructure applications, offering a low-energy, low-carbon footprint solution. This study evaluates five models: Random Forest (RF), Robust Linear Regression (RL), Recurrent Neural Network (RNN), Response Surface Methodology (RSM), and Regression Tree (RT). The RL and RT models were utilized in the prediction of GPC Compressive strength (CS), employing the Matlab R19a regression learner APP. The RNN model was implemented using the Matlab R19a toolkit. Furthermore, the RF model was developed using R studio version 4.2.2 programming code, and the RSM model was constructed using the Minitab 18 toolbox. EViews 12 software was utilized for both pre-processing and post-processing of the data. Additionally, it was employed to convert the non-stationary data into stationary data in order to obtain accurate results. The input variables included SiO2/Na2O (S/N), Na2O (N), Water/Binder Ratio (W/B), Curing Time (CT), Ultrasonic Pulse Velocity (UPV), and 28-day Compressive Strength (Mpa) (CS) as the target variable. The findings of the study indicate that the RMS-M3 model exhibited superior performance compared to all other models, demonstrating a high level of accuracy. Specifically, the Pearson correlation coefficient (PCC) was calculated to be 0.994, while the mean absolute percentage error (MAPE) was found to be 0.708 during the verification phase.

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New random intelligent chemometric techniques for sustainable geopolymer concrete: Low-energy and carbon-footprint initiatives

Jibril,

Malami,

Jibrin

et al. 2023

Preprint

0

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The construction industry, being a significant contributor to greenhouse gas emissions, facing considerable attention and demand on account of the increasing global apprehension regarding climate change and its adverse impacts on environments. Geopolymer shows itself as a viable and sustainable alternative to the Portland cement binder in civil infrastructure applications, offering a low-energy, low-carbon footprint solution. This study evaluates five models: Random Forest (RF), Robust Linear Regression (RL), Recurrent Neural Network (RNN), Response Surface Methodology (RSM), and Regression Tree (RT). The RL and RT models were utilized in the prediction of GPC Compressive strength (CS), employing the Matlab R19a regression learner APP. The RNN model was implemented using the Matlab R19a toolkit. Furthermore, the RF model was developed using R studio version 4.2.2 programming code, and the RSM model was constructed using the Minitab 18 toolbox. EViews 12 software was utilized for both pre-processing and post-processing of the data. Additionally, it was employed to convert the non-stationary data into stationary data in order to obtain accurate results. The input variables included SiO2/Na2O (S/N), Na2O (N), Water/Binder Ratio (W/B), Curing Time (CT), Ultrasonic Pulse Velocity (UPV), and 28-day Compressive Strength (Mpa) (CS) as the target variable. The findings of the study indicate that the RMS-M3 model exhibited superior performance compared to all other models, demonstrating a high level of accuracy. Specifically, the Pearson correlation coefficient (PCC) was calculated to be 0.994, while the mean absolute percentage error (MAPE) was found to be 0.708 during the verification phase.

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Prediction of Geopolymer Concrete Compressive Strength Utilizing Artificial Neural Network and Nondestructive Testing

Almasaeid

¹

,

Alkasassbeh

²

,

Yasin

³

2022

Civil and Environmental Engineering

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A promising substitute for regular concrete is geopolymer concrete. Engineering mechanical parameters of geopolymer concrete, including compressive strength, are frequently measured in the laboratory or in-situ via experimental destructive tests, which calls for a significant quantity of raw materials, a longer time to prepare the samples, and expensive machinery. Thus, to evaluate compressive strength, non-destructive testing is preferred. Therefore, the objective of this research is to develop an artificial neural network model based on the results of destructive and non-destructive tests to assess the compressive strength of geopolymer concrete without needing further destructive tests. According to the artificial neural network analysis developed in this study, the compressive strength of geopolymer concrete can be predicted rather accurately by combining the results of the non-destructive with R 2 of 0.9286.

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Geopolymer Concrete: A Review

Cited by 13 publications

References 3 publications

New random intelligent chemometric techniques for sustainable geopolymer concrete: low-energy and carbon-footprint initiatives

New random intelligent chemometric techniques for sustainable geopolymer concrete: low-energy and carbon-footprint initiatives

New random intelligent chemometric techniques for sustainable geopolymer concrete: Low-energy and carbon-footprint initiatives

Prediction of Geopolymer Concrete Compressive Strength Utilizing Artificial Neural Network and Nondestructive Testing

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