Dataset Information

Influence of Fly Ash Additive on the Properties of Concrete with Slag Cement.

ABSTRACT: This paper presents research on the impact of fly ash addition on selected physical and mechanical parameters of concrete made with slag cement. Experimental tests were carried out to measure the migration of chloride ions in concrete, the tightness of concrete exposed to water under pressure, and the compressive strength and tensile strength of concrete during splitting. Six series of concrete mixes made with CEM IIIA 42.5 and 32.5 cement were tested. The base concrete mix was modified by adding fly ash as a partial cement substitute in the amounts of 25% and 33%. A comparative analysis of the obtained results indicates a significant improvement in tightness, especially in concrete based on CEM IIIA 32.5 cement and resistance to chloride ion penetration for the concretes containing fly ash additive. In the concretes containing fly ash additive, a slower rate of initial strength increase and high strength over a long period of maturation are shown. In accordance with the presented research results, it is suggested that changes to the European standardization system be considered, to allow the use of fly ash additive in concrete made with CEM IIIA 42.5 or 32.5 cement classes. Such a solution is not currently acceptable in standards in some European Countries.

SUBMITTER: Szczesniak A

PROVIDER: S-EPMC7435652 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Influence of Fly Ash Additive on the Properties of Concrete with Slag Cement.

Szcześniak Anna A Zychowicz Jacek J Stolarski Adam A

Materials (Basel, Switzerland) 20200723 15

This paper presents research on the impact of fly ash addition on selected physical and mechanical parameters of concrete made with slag cement. Experimental tests were carried out to measure the migration of chloride ions in concrete, the tightness of concrete exposed to water under pressure, and the compressive strength and tensile strength of concrete during splitting. Six series of concrete mixes made with CEM IIIA 42.5 and 32.5 cement were tested. The base concrete mix was modified by addin ...[more]

PMID: 32717849

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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 Fly Ash Additive on the Properties of Concrete with Slag Cement.

Publications

Influence of Fly Ash Additive on the Properties of Concrete with Slag Cement.

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