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Fly ash/Kaolin based geopolymer green concretes and their mechanical properties.

ABSTRACT: Geopolymer concrete mixes were cast using fly ash, kaolin, sodium hydroxide, potassium hydroxide, sodium silicate and aggregates. Portland cement concrete (M30) was used as a reference sample. The effect of silica fume, temperature (40 °C, 60 °C, 80 °C, 100 °C and 120 °C), sodium and potassium hydroxides and different superplasticizers on the compressive strength are reported [1]. Maximum strength was found at 100 °C and 14 M alkali solution [1].

SUBMITTER: Okoye FN

PROVIDER: S-EPMC4659805 | biostudies-other | 2015 Dec

REPOSITORIES: biostudies-other

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Fly ash/Kaolin based geopolymer green concretes and their mechanical properties.

Okoye F N FN Durgaprasad J J Singh N B NB

Data in brief 20151107

Geopolymer concrete mixes were cast using fly ash, kaolin, sodium hydroxide, potassium hydroxide, sodium silicate and aggregates. Portland cement concrete (M30) was used as a reference sample. The effect of silica fume, temperature (40 °C, 60 °C, 80 °C, 100 °C and 120 °C), sodium and potassium hydroxides and different superplasticizers on the compressive strength are reported [1]. Maximum strength was found at 100 °C and 14 M alkali solution [1]. ...[more]

PMID: 26693505

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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

Fly ash/Kaolin based geopolymer green concretes and their mechanical properties.

Publications

Fly ash/Kaolin based geopolymer green concretes and their mechanical properties.

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