Investigating the Influence of Recycled Coarse Aggregate and Steel Fiber on the Rheological and Mechanical Properties of Self-Compacting Geopolymer Concrete
Bibliographic record
Abstract
This experimental study presents the effect of steel fiber (SF) and recycled coarse aggregate (RCA) on the fresh and hardened properties of Self-compacting geopolymer concrete (SCGC).Sixteenth alkali-activated based metakaolin (MK) concrete mixtures with constant binder content of 500 kg/m 3 incorporated 0, 0.5, 1.0, and 1.5% volume fraction of SF and 0, 10, 20, and 30% RCA as a partially replacement for natural coarse aggregate (NCA) with sodium hydroxide concentration of 12 Molarity at ambient conditions.Fresh state of SCGC were examined slump flow, T500 flow, V-funnel, and Lbox test.At 28 and 90 days the compressive strength and splitting tensile strength were investigated, the flexural strength was evaluated at 90 days.The results highlighted that RCA (30%) and SF (1.5%) significantly constrain the fresh properties of SCGC mixes.Moreover, SCGC incorporated RCA can be produced with compressive strength as high as 31.91-41.13at 28 and 90 days, respectively.However, 1% SF and 30% RCA in MKbased SCGC appear better performance than the control mixture and leads to an ecologically friendly concrete mix that has appropriate hardening properties and that would contribute to the longevity of the construction industry.
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".