Investigation of Alkali-Activated Slag-Based Composite Incorporating Dehydrated Cement Powder and Red Mud
Bibliographic record
Abstract
Recycled construction cementitious materials (RCCM) and red mud (RM) could be considered a type of discarded material with potential cementitious properties. Generally, landfilling and stacking are utilized to dispose of this type of solid waste, which can be detrimental to the environment and sustainability of the construction sector. Accordingly, a productive process for making eco-efficient alkali-activated slag-based samples with the inclusion of RCCM and red mud is studied in this paper. Dehydrated cement powder (DCP) is attained through the high-temperature treatment of RCCM, and red mud can be obtained from the alumina industry. Subsequently, DCP and RM are utilized as a partial substitute for granulated blast furnace slag (GBFS) in alkali-activated mixtures. Two different batches were designed; the first batch had only DCP at a dosage of 15%, 30%, 45%, and 60% as a partial substitute for GBFS, and the second batch had both DCP and RM at 15%, 30%, 45%, and 60% as a partial substitute for GBFS. Different strength and durability characteristics were assessed. The findings show that when both dehydrated cement powder and red mud are utilized in high quantities, the strength and durability of the specimens were enhanced, with compressive strength improving by 42.2% at 28 days. Such improvement was obtained when 7.5% each of DCP and RM were added. The results revealed that DCP and RM have a negative effect on workability, whilst they had a positive impact on the drying shrinkage as well as the mechanical strength. X-ray diffraction and micro-structural analysis showed that when the amount of DCP and RM is increased, a smaller number of reactive products forms, and the microstructure was denser than in the case of the samples made with DCP alone. It was also confirmed that when DCP and RM are used at optimized dosages, they can be a potential sustainable binder substitute; thus, valorizing wastes and inhibiting their negative environmental footprint.
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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 | 0.000 | 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".