MECHANICAL PROPERTIES OF SELF-COMPACTING GEOPOLYMER CONCRETE CONTAINING SPENT GARNET AS REPLACEMENT FOR FINE AGGREGATE
Bibliographic record
Abstract
Millions of tons of spent garnet, a by-product of surface treatment operations, are disposed of in landfills, oceans, rivers, and quarries, among others every year, thus it causes environmental problems. The main objective of this study is to evaluate spent garnet as a sand replacement in concrete prepared with ground granulated blast furnace slag (GGBS)-based self-compacting geopolymer concrete (SCGC). Concrete mixtures containing 0%, 25%, 50%, 75% and 100% spent garnet as a replacement for river sand were prepared with a constant Liquid/Binder (L/B) mass ratio equal to 0.4. Compressive, flexural and splitting tensile strengths as well as workability tests (slump, L-box, U-box and T50) were conducted on concrete containing spent garnet. As per specification and guidelines for self-compacting concrete (EFNARC) standard, the test results showed that the concrete’s workability increased with the increase of spent garnet, while all the other strength values were consistently lower than conventional concrete (SCGC) at all stages of replacement. The results recommended that spent garnet should be used in concrete as a sand replacement up to 25% to reduce environmental problems, costs and the depletion of natural resources.
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 | 0.001 | 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".