The behavior of post-fire cured alkali-activated slag incorporating self-healing additive
Bibliographic record
Abstract
The durability of alkali-activated slag (AAS) binders has garnered significant attention due to their sustainable and environmentally friendly properties. However, exposure to elevated temperatures during fires can severely compromise the structural integrity of AAS-based materials. This study investigated the potential of a self-healing additive (crystalline admixture CA) to enhance the autogenous self-healing capacity of fire damaged AAS. A series of AAS specimens, with and without CA, were exposed to varying temperatures from 200 ºC up to 800 ºC. Self-healing efficiency was evaluated through tests, including compressive strength, tensile strength, water absorption, and permeability, focusing on autogenous self-healing mechanisms. Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM/EDS) was also implemented to determine the morphology of the produced self-healing Products. Results showed that incorporating CA into AAS significantly improved its self-healing performance post-fire exposure, particularly at higher temperatures (i.e. more than 600 ºC). The CA addition enhanced the formation of secondary hydration products and crystalline structures within the cracks, reducing permeability and promoting mechanical strength recovery. The study highlights the potential of CA as an effective solution to mitigate post-fire damages in AAS, providing a pathway toward more resilient and sustainable construction materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".