Rice husk ash as supplementary cementing material to inhibit the alkali-silica reaction in mortars
Bibliographic record
Abstract
abstract: Alkali-silica reaction (ASR) is one of the most harmful distress mechanisms that affects the durability of concrete worldwide. Yet, it has been found that ASR-induced expansion and distress may be prevented by the appropriate use of supplementary cementing materials (SCMs). Recent studies suggest that rice husk ash (RHA), a by-product of the rice production, may present promising performances as a pozzolanic material and to enhance the durability of blended mixtures. There are many controversial studies regarding the use of RHA and how its properties directly affect the performance and durability of concretes and mortars. The present study aims to evaluate the influence of the RHA on ASR through the accelerated mortar bar test (AMBT), X-Rays Diffraction (XRD), Thermogravimetric Analysis (TG), Mercury Intrusion Porosimetry (MIP) and Scanning Electron Microscopy (SEM). To validate the obtained data, the results were compared with mortars made of a well-known SCM (silica fume) which has distinguished behaviour against ASR. The results indicate that, besides the silica fume mortars have showed better results, the use of RHA suggests promising results to mitigate the ASR, yet, further analysis on concrete prims test should be carried out to fully validate the use of RHA to enhance the durability of the concrete. The RHA finesses and its particle size were the most important properties in the SCM performance.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".