The Role of SiO
 2
 and Al
 2
 O
 3
 on the Properties of Geopolymers with and without Calcium

Silva, P. De; Hanjitsuwan, Sakonwan; Chindaprasirt, Prinya

doi:10.1002/9781118807743.ch3

Cited by 5 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e chemical composition of the source materials, alkali concentration, and percentage replacement of FA by GGBFS are major factors which influences the strength of FA-slag geopolymer. e chemical components SiO 2 , Al 2 O 3 , Na 2 O, and CaO of the source materials have significant effect on the properties of geopolymer [18]. Chemical optimisation of Si : Al and Na : Al molar ratios is also found to have an effect on compressive strength [19].…”

Section: Introductionmentioning

confidence: 95%

A New Artificial Neural Network Model for the Prediction of the Effect of Molar Ratios on Compressive Strength of Fly Ash‐Slag Geopolymer Mortar

John

Cascardi

Nadir

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

Geopolymers are inorganic polymers produced by the alkali activation of alumina-silicate minerals. Geopolymer is an alternative cementitious binder to traditional Ordinary Portland Cement (OPC) leading to economical and sustainable construction technique by the utilisation of alumina-silicate waste materials. The strength development in fly ash-slag geopolymer mortar is dependent on the chemical composition of the raw materials. An effective way to study the effect of chemical components in geopolymer is through the evaluation of molar ratios. In this study, an Artificial Neural Network (ANN) model has been applied to predict the effect of molar ratios on the 28-day compressive strength of fly ash-slag geopolymer mortar. For this purpose, geopolymer mortar samples were prepared with different fly ash-slag composition, activator concentration, and alkaline solution ratios. The molar ratios of the geopolymer mortar samples were evaluated and given as input to ANN, and the compressive strength was obtained as the output. The accuracy of the assessed model was investigated by statistical parameters; the mean, median, and mode values of the ratio between actual and predicted strength are equal to 0.991, 0.973, and 0.991, respectively, with a 14% coefficient of variation and a correlation coefficient of 89%. Based on the mentioned findings, the proposed novel model seems reliable enough and could be used for the prediction of compressive strength of fly ash-slag geopolymer. In addition, the influence of molar compositions on the compressive strength was further investigated through parametric studies utilizing the proposed model. The percentages of Na2O and SiO2 of the source materials were observed as the dominant chemical compounds in the mix affecting the compressive strength. The influence of CaO was significant when combined with a high amount of SiO2 in alkaline solution.

show abstract

Section: Introductionmentioning

confidence: 95%

A New Artificial Neural Network Model for the Prediction of the Effect of Molar Ratios on Compressive Strength of Fly Ash‐Slag Geopolymer Mortar

John

Cascardi

Nadir

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

3D-printable alkali-activated concretes for building applications: A critical review

Amran

Abdelgader

Onaizi

et al. 2022

Construction and Building Materials

View full text Add to dashboard Cite

Assessment of the environmental impacts of utilizing coal ashes and OPC for soil stabilization applications: Leachate analysis in response to rainfall interaction

Hauashdh,

Radin Mohamed,

Nagapan

et al. 2024

Case Studies in Construction Materials

View full text Add to dashboard Cite

The Role of SiO ₂ and Al ₂ O ₃ on the Properties of Geopolymers with and without Calcium

Cited by 5 publications

References 20 publications

A New Artificial Neural Network Model for the Prediction of the Effect of Molar Ratios on Compressive Strength of Fly Ash‐Slag Geopolymer Mortar

A New Artificial Neural Network Model for the Prediction of the Effect of Molar Ratios on Compressive Strength of Fly Ash‐Slag Geopolymer Mortar

3D-printable alkali-activated concretes for building applications: A critical review

Assessment of the environmental impacts of utilizing coal ashes and OPC for soil stabilization applications: Leachate analysis in response to rainfall interaction

Contact Info

Product

Resources

About

The Role of SiO 2 and Al 2 O 3 on the Properties of Geopolymers with and without Calcium

Cited by 5 publications

References 20 publications

A New Artificial Neural Network Model for the Prediction of the Effect of Molar Ratios on Compressive Strength of Fly Ash‐Slag Geopolymer Mortar

A New Artificial Neural Network Model for the Prediction of the Effect of Molar Ratios on Compressive Strength of Fly Ash‐Slag Geopolymer Mortar

3D-printable alkali-activated concretes for building applications: A critical review

Assessment of the environmental impacts of utilizing coal ashes and OPC for soil stabilization applications: Leachate analysis in response to rainfall interaction

Contact Info

Product

Resources

About

The Role of SiO ₂ and Al ₂ O ₃ on the Properties of Geopolymers with and without Calcium