Compressive Strength and Microstructural Development of Cementitious Mixtures Incorporating Ultrafine Granulated Blast Furnace Slag
Bibliographic record
Abstract
Utilization of ultrafine supplementary cementitious materials (SCMs) instead of ordinary ones has shown great potential to enhance the strength development of concrete mixtures. Replacing large amounts of portland cement with ultrafine SCMs, however, may not be feasible due to their negative effect on the water demand of concrete, and extra cost and energy consumption associated with the ultrafine grinding of SCMs. It is therefore important to explore practical methods for efficient use of ultrafine SCMs in producing low portland cement content concretes. To achieve this goal, the current study focused on the use of ultrafine granulated blast furnace slag (GBFS) in combination with a commercial one to replace 30%, 40%, and 50% by weight of portland cement in preparing mortar samples. The compressive strength of the mortars was measured at different ages ranging from 1 to 91 days. Cement paste samples with modified (combination of ultrafine and commercial) and commercial GBFS were also prepared and tested by the isothermal calorimetry, thermogravimetric analysis (TGA), X-ray diffraction (XRD), and scanning electron microscopy equipped with energy dispersive X-ray spectroscopy (SEM/EDS) techniques. The results showed that the samples with 30% and 40% by weight modified GBFS as portland cement replacement had higher compressive strength compared with those made with the same amount of commercial GBFS or 100% by weight portland cement at all the testing ages. The microstructural analyses indicated increased calcium hydroxide consumption and higher reaction degree of clinker phases after 1 day of hydration for the sample incorporating 40% by weight modified GBFS compared with that with the same amount of commercial one, resulting in the superior compressive strength of this sample at such an early age of hydration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".