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Experimental and Statistical Study on Mechanical Characteristics of Geopolymer Concrete.

ABSTRACT: This paper studies the statistical correlation in mechanical characteristics of class F fly ash based geopolymer concrete (CFGPC). Experimentally measured values of the compressive strength, elastic modulus and indirect tensile strength of CFGPC specimens made from class F fly ash (CFA) were presented and analyzed. The results were compared with those of corresponding ordinary Portland cement concrete (OPCC) using statistical hypothesis tests. Results illustrated that when possessing similar compressive and tensile strength, the elastic modulus for CFGPC is significantly lower than that of OPCC. The corresponding expressions recommended by standards for the case of OPCC is proved to be inaccurate when applied in the case of CFGPC. Statistical regression was used to identify tendencies and correlations within the mechanical characteristics of CFGPC, as well as the empirical equations for predicting tensile strength and elastic modulus of CFGPC from its compressive strength values. In conclusion, CFGPC and OPCC has significant differences in terms of the correlations between mechanical properties. The empirical equations obtained in this study could provide relatively accurate predictions on the mechanical behavior of CFGPC.

SUBMITTER: Cui Y

PROVIDER: S-EPMC7178337 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Experimental and Statistical Study on Mechanical Characteristics of Geopolymer Concrete.

Cui Yifei Y Gao Kaikai K Zhang Peng P

Materials (Basel, Switzerland) 20200402 7

This paper studies the statistical correlation in mechanical characteristics of class F fly ash based geopolymer concrete (CFGPC). Experimentally measured values of the compressive strength, elastic modulus and indirect tensile strength of CFGPC specimens made from class F fly ash (CFA) were presented and analyzed. The results were compared with those of corresponding ordinary Portland cement concrete (OPCC) using statistical hypothesis tests. Results illustrated that when possessing similar com ...[more]

PMID: 32252367

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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.

Project description:The aim of this study was to develop new sulfur-copolymer concrete composites using waste compounds that have good mechanical characteristics and show a resistance to biocorrosion. The comonomers used to synthesize the sulfur-organic copolymers were-90 wt. % sulfur; 5 wt. % dicyclopentadiene (DCPD); 5 wt. % organic monomers, styrene (SDS), 1-decene (SDD), turpentine (SDT), and furfural (SDF). The concrete composites based on sulfur-organic copolymers were filled with aggregates, sand, gravel, as well as additives and industrial waste such as fly ash or phosphogypsum. The sulfur-organic copolymers were found to be chemically stable (softening temperature, thermal stability, melting temperature, amount of recrystallized sulfur, and shore D hardness). Partial replacement of DCPD with other organic comonomers did not change the thermal stability markedly but did make the copolymers more elastic. However, the materials became significantly stiffer after repeated melting. All the tested copolymers were found to be resistant to microbial corrosion. The highest resistance was exhibited by the SDS-containing polymer, while the SDF polymer exhibited the greatest change due to the activity of the microorganisms (FTIR analysis and sulfur crystallization). The concrete composites with sulfur-organic copolymers containing DCPD, SDS, SDF, fly ash, and phosphogypsum were mechanically resistant to compression and stretching, had low water absorbance, and were resistant to factors, such as temperature and salt. Resistance to freezing and thawing (150 cycles) was not confirmed. The concrete composites with sulfur-organic copolymers showed resistance to bacterial growth and acid activity during 8 weeks of incubation with microorganisms. No significant structural changes were observed in the SDS composites after incubation with bacteria, whereas composites containing SDF showed slight changes (FTIR and microscopic analysis). The concrete composite containing sulfur, DCPD, SDS, sand, gravel, and fly ash was the most resistant to microbiological corrosion, based on the metabolic activity of the bacteria and the production of ergosterol by the molds after eight weeks of incubation. It was found that Thiobacillus thioparus was the first of the acidifying bacteria to colonize the sulfur concrete, decreasing the pH of the environment. The molds Penicillium chrysogenum, Aspergillus versicolor and Cladosporium herbarum were able to grow on the surface of the tested composites only in the presence of an organic carbon source (glucose). During incubation, they produced organic acids and acidified the environment. However, no morphological changes in the concretes were observed suggesting that sulfur-organic copolymers containing styrene could be used as engineering materials or be applied as binders in sulfur-concretes.

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Experimental and Statistical Study on Mechanical Characteristics of Geopolymer Concrete.

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Experimental and Statistical Study on Mechanical Characteristics of Geopolymer Concrete.

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