Non-traditional aluminosilicate based alkali-activated mortars - statistical optimization of solution parameters and processing conditions for optimal compressive strength, workability and setting time

CONSTRUCTION AND BUILDING MATERIALS(2023)

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摘要
The recent shortage of traditional fly ash and slag, commonly used precursors in alkali-activated binders, prompted a rigorous search for alternative materials. This study evaluated four groups of currently underused supplementary cementitious materials (SCMs) as precursors for producing alkali-activated mortars. The materials evaluated include three calcined clays, three ground bottom ashes, three volcanic ashes, and two fluidized bed combustion ashes, collectively named non-traditional and natural pozzolans (NNPs). Firstly, the compositions of the NNPs were optimized using trial-and-error methods to minimize the concentration of the activating solution. Then, the two solution parameters i.e., silica modulus and Na2O% were optimized for compressive strength using a central composite design. The compressive strength (ASTM C109), the workability (ASTM C1437), and the setting time (ASTM C191) of the mortar were determined. The results show that all 11 materials could produce mortar mixtures with compressive strength in the 20-40 MPa range at 28 days, satisfactory workability of at least 50 % (flow value), and at least 30 min of initial setting time without heat curing and without blending with conventional SCMs. Most of the materials were activated with lower solution parameters than those used in the literature. The binder optimization suggested Si/Al range 1.0-3.0 and a minimum Ca/Al of 0.25 as favorable molar ratios for potentially reactive aluminosilicate precursors. Multiple linear regression models validated by higher R2 and minimum RMSE and MAE could accurately predict the compressive strength of NNP mortar. Furthermore, calcium hydroxide and shrinkage-reducing admixtures were identified as set regulators in alkali activated NNP mortars.
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关键词
Alkali activated mortar,NNP,DoE,Correlation,Regression,Compressive strength,Flow,Setting time
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