Dataset Information

Influence of Activators on Mechanical Properties of Modified Fly Ash Based Geopolymer Mortars.

ABSTRACT: The aim of this work was to study the influence of the type of activator on the formulation of modified fly ash based geopolymer mortars. Geopolymer and alkali-activated materials (AAM) were made from fly ashes derived from coal and biomass combustion in thermal power plants. Basic activators (NaOH, CaO, and Na2SiO3) were mixed with fly ashes in order to develop binding properties other than those resulting from the use of Portland cement. The results showed that the mortars with 5 mol/dm3 of NaOH and 100 g of Na2SiO3 (N5-S22) gave a greater compressive strength than other mixes. The compressive strengths of analyzed fly ash mortars with activators N5-S22 and N5-C10 (5 mol/dm3 NaOH and 10% CaO) varied from 14.3 MPa to 5.9 MPa. The better properties of alkali-activated mortars with regular fly ash were influenced by a larger amount of amorphous silica and alumina phases. Scanning electron microscopy and calorimetry analysis provided a better understanding of the observed mechanisms.

SUBMITTER: Prochon P

PROVIDER: S-EPMC7084247 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Influence of Activators on Mechanical Properties of Modified Fly Ash Based Geopolymer Mortars.

Prochon Piotr P Zhao Zengfeng Z Courard Luc L Piotrowski Tomasz T Michel Frédéric F Garbacz Andrzej A

Materials (Basel, Switzerland) 20200225 5

The aim of this work was to study the influence of the type of activator on the formulation of modified fly ash based geopolymer mortars. Geopolymer and alkali-activated materials (AAM) were made from fly ashes derived from coal and biomass combustion in thermal power plants. Basic activators (NaOH, CaO, and Na<sub>2</sub>SiO<sub>3</sub>) were mixed with fly ashes in order to develop binding properties other than those resulting from the use of Portland cement. The results showed that the mortar ...[more]

PMID: 32106414

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Project description:The emission of greenhouse gases and natural-resource depletion caused by the production of ordinary Portland cement (OPC) have a detrimental effect on the environment. Thus, an alternative means is required to produce eco-friendly concrete such as geopolymer concrete (GPC). However, GPC has a complex cementitious matrix and an ambiguous mix design. Aside from that, the composition and proportions of materials utilized may have an impact on the compressive strength. Similarly, the use of robust and efficient machine-learning (ML) approaches is now required to forecast the strength of such a composite cementitious matrix. As a result, this study anticipated the compressive strength of GPC with waste resources using ensemble and non-ensemble ML algorithms. This was accomplished through the use of Anaconda (Python). To build a strong ensemble learner by integrating weak learners, adaptive boosting, random forest (RF), and ensemble learner bagging were employed. Furthermore, ensemble learners were utilized on non-ensemble or weak learners, such as decision trees (DT) and support vector machines (SVM) via regression. The data encompassed 156 statistical samples in which nine variables, namely superplasticizer (kg/m3), fly ash (kg/m3), ground granulated blast-furnace slag (GGBS), temperature (°C), coarse and fine aggregate (kg/m3), sodium silicate (Na2SiO3), and sodium hydroxide (NaOH), were chosen to anticipate the results. Exploring it in depth, twenty sub-models with ensemble boosting and bagging approaches were trained, and tuning was performed to achieve the highest possible coefficient of determination (R2). Moreover, cross K-Fold validation analysis and statistical checks were performed via indicators for the evaluation of the models. The result revealed that ensemble approaches yielded robust performance compared to non-ensemble algorithms. Generally, an ensemble learner with the RF and bagging approach on a DT yielded robust performance by achieving a better R2 as 0.93, and with the lowest statistical errors. The communal model in artificial-intelligence analysis, on average, improved the accuracy of the model.

Dataset Information

Influence of Activators on Mechanical Properties of Modified Fly Ash Based Geopolymer Mortars.

Publications

Influence of Activators on Mechanical Properties of Modified Fly Ash Based Geopolymer Mortars.

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