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Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar

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...

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Autores principales: Ahmed, Hemn Unis, Abdalla, Aso A., Mohammed, Ahmed S., Mohammed, Azad A., Mosavi, Amir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8911711/
https://www.ncbi.nlm.nih.gov/pubmed/35269099
http://dx.doi.org/10.3390/ma15051868
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author Ahmed, Hemn Unis
Abdalla, Aso A.
Mohammed, Ahmed S.
Mohammed, Azad A.
Mosavi, Amir
author_facet Ahmed, Hemn Unis
Abdalla, Aso A.
Mohammed, Ahmed S.
Mohammed, Azad A.
Mosavi, Amir
author_sort Ahmed, Hemn Unis
collection PubMed
description 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 (R(2)), 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, R(2), 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 SiO(2) percentage of the FA and the alkaline liquid-to-binder ratio of the mixture.
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spelling pubmed-89117112022-03-11 Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar Ahmed, Hemn Unis Abdalla, Aso A. Mohammed, Ahmed S. Mohammed, Azad A. Mosavi, Amir Materials (Basel) Article 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 (R(2)), 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, R(2), 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 SiO(2) percentage of the FA and the alkaline liquid-to-binder ratio of the mixture. MDPI 2022-03-02 /pmc/articles/PMC8911711/ /pubmed/35269099 http://dx.doi.org/10.3390/ma15051868 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ahmed, Hemn Unis
Abdalla, Aso A.
Mohammed, Ahmed S.
Mohammed, Azad A.
Mosavi, Amir
Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar
title Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar
title_full Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar
title_fullStr Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar
title_full_unstemmed Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar
title_short Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar
title_sort statistical methods for modeling the compressive strength of geopolymer mortar
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8911711/
https://www.ncbi.nlm.nih.gov/pubmed/35269099
http://dx.doi.org/10.3390/ma15051868
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