Evaluating the Impact of Inclusion Metakaolin and Silica Fume on the Green and Mechanical Properties of Low Calcium Fly Ash Concrete
Bibliographic record
Abstract
Generally, low calcium fly ash concretes (FFACs) subjected to ambient temperatures exhibit low initial strengths. Thus, FFACs require higher temperatures to improve the monomer dissolutions, but that increases the energy consumption. Thus, this paper aims to evaluate the influence of the incorporation of different dosages of silica fume (SF) and metakaolin (MK) on the green and hardened properties of FFACs cured at room temperature of 21 ˚C. Slump and air content tests were applied to evaluate the green characteristics of the designed geopolymer concrete mixes (GPCMs). The hardened properties of FFACs were assessed in terms of compressive, flexural, and splitting tensile strengths at different curing ages. Outcomes revealed that the slump and air content of GPCMs declined with improving SF or MK percentages. The 28 days flexural, compressive, and splitting tensile strengths of FFACs manufactured with the optimum dosages of 20% MK and 10% SF cured at 21 ˚C were evidently higher than those of FFACs (100 % FFA) subjected to curing temperatures ranging from 10-to-50 ˚C. Also, the strength enhancement of MK was relatively comparable to those of SF with less requirement for superplasticizers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".