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Investigation on the key mix design parameters of slag-based geopolymer concrete
In this study, the Taguchi method was utilized to investigate the key mix design parameters on both workability and compressive strength of geopolymer concrete (GPC) mixes through different curing regimes. The used aluminosilicate source was ground granulated blast furnace slag (GGBFS), and a combin...
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Published in: | Innovative infrastructure solutions : the official journal of the Soil-Structure Interaction Group in Egypt (SSIGE) 2024-06, Vol.9 (6), Article 230 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | In this study, the Taguchi method was utilized to investigate the key mix design parameters on both workability and compressive strength of geopolymer concrete (GPC) mixes through different curing regimes. The used aluminosilicate source was ground granulated blast furnace slag (GGBFS), and a combination of sodium hydroxide (SH) and sodium silicate (SS) was employed as the alkaline activator. The study focused on four key parameters: binder content, water-to-binder ratio (W/B), aggregate type, and curing regime. A total of nine experimental series were conducted using the L9 Taguchi array. The compressive strength test and slump test, representing workability, were carried out for all nine mixes, and their response index was evaluated using the signal-to-noise (S/N) ratio to obtain the optimum level for each parameter. Among all parameters investigated in this study, the results indicate that the W/B ratio was the most predominant parameter influencing the 7-day and 28-day compressive strength, and workability of the developed GPC mixes, with the highest S/N ratio value. Although the effect of the aggregate type was minor, the gravel achieved the best workability, while the crushed stone of size number 6, Sen1, achieved the best compressive strength. Heat curing was not an effective regime for the late age of GGBFS-based GPC. |
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ISSN: | 2364-4176 2364-4184 |
DOI: | 10.1007/s41062-024-01473-x |