• Journal of the Korea Concrete Institute
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• v.13 no.3
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• pp.213-220
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• 2001

Incorporation of wastes glass aggregate in mortar may cause crack and this may result in the strength reduction due to alkali-silica reaction(ASR) and expansion. The purposes of this study were to investigate the properties of alkali-silica expansion and strength loss through a series of experiments which had a main experimental variables such as waste glass aggregate contents, glass colors, fiber types, and fiber contents. The steel fibers and polypropylene fibers were used for constraining the ASR expansion and mortar cracking. From the result, green waste glass was more suitable than brown one because of low expansion. And in this accelerated ASTM C 1260 test of waste glass, pessimum content can not be found. Also, when used the fibers with waste glass, there is an effect on reduction of expansion and strength loss due to ASR between the alkali in the cement paste and the silica in the waste glass. Specially, adding 1.5 vol.% of steel fiber to 20% of waste glass, the expansion ratio was reduced by 40% and flexural strength was developed by up to 110% comparing with only waste glass(80$\^{C}$ H$_2$O curing).

• Journal of the Mineralogical Society of Korea
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• v.17 no.3
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• pp.277-288
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• 2004

Alkali-silica reaction (ASR) is a chemical reaction between alkalies in cement and chemically unstable aggregates and causes expansion and cracking of concrete. In the Present study, we studied the effects of metakaolin, which is a newly introduced mineral admixture showing excellent pozzolainc reaction property, on the inhibition of ASR. We prepared mortar-bars of various replacement ratios of metakaolin and conducted alkali-silica reactivity test (ASTM C 1260), compressive strength test and flow test. We also carefully analyzed the mineralogical changes in hydrate cement paste by XRD qualitative analysis. The admixing of metakaolin caused quick pozzolanic reaction and hydration reaction that resulted in a rapid decrease in portlandite content of hydrated cement paste. The expansion by ASR was reduced effectively as metakaolin replaced cement greater than 15%. This resulted in that the amounts of available portlandite decreased to less than 10% in cement paste. It is considered that the inhibition of ASR expansion by admixing of metakaolin was resulted by the combined processes that the formation of deleterious alkali-calcium-silicate gel was inhibited and the penetration of alkali solution into concrete was retarded due to the formation of denser, more homogeneous cement paste caused by pozzolanic effect. Higher early strength (7 days) than normal concrete was developed when the replacement ratios of metakaolin were greater than 15%. And also, late strength (28 days) was far higher than normal concrete for the all the replacement ratios of metakaolin. The development patterns of mechanical strength for metakaolin admixed concretes reflect the rapid pozzolanic reaction and hydration properties of metakaolin.

• Structural Engineering and Mechanics
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• v.83 no.1
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• pp.79-92
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• 2022

In this paper, the impact of a vernacular pozzolanic kaolinite mine on concrete alkali-silica reaction and strength has been evaluated. For making the samples, kaolinite powder with various levels has been used in the quality specification test of aggregates based on the ASTM C1260 standard in order to investigate the effect of kaolinite particles on reducing the reaction of the mortar bars. The compressive strength, X-Ray Diffraction (XRD) and Scanning Electron Microscope (SEM) experiments have been performed on concrete specimens. The obtained results show that addition of kaolinite powder to concrete will cause a pozzolanic reaction and decrease the permeability of concrete samples comparing to the reference concrete specimen. Further, various machine learning methods have been used to predict ASR-induced expansion per different amounts of kaolinite. In the process of modeling methods, optimal method is considered to have the lowest mean square error (MSE) simultaneous to having the highest correlation coefficient (R). Therefore, to evaluate the efficiency of the proposed model, the results of the support vector machine (SVM) method were compared with the decision tree method, regression analysis and neural network algorithm. The results of comparison of forecasting tools showed that support vector machines have outperformed the results of other methods. Therefore, the support vector machine method can be mentioned as an effective approach to predict ASR-induced expansion.