Mechanical Characteristics and Durability of Metakaolin-Based Self-Compacting Geopolymer Concrete as A Function of Recycled Aggregate and Steel Fiber Contents
Bibliographic record
Abstract
There has been a significant interest in the development of eco-friendly building materials.Recyclable and environmentally friendly, geopolymer composites are extraordinary binding materials.The purpose of this experimental study was to examine the mechanical and durability properties of metakaolin based self-compacting geopolymer concrete (SCGPC) comprising steel fibers (SF) and recycled aggregate concrete (RCA) varying percentages of recycled coarse aggregate.The mechanical and durability properties of the geopolymer composites, including fracturing tensile strength, and flexural strength, were subsequently evaluated.At weight proportions of 0%, 25%, 50%, 75% and 100%, the recycled coarse aggregates were substituted for the natural coarse aggregates.The amounts of SF incorporated into the mixtures were 0, 0.5, 1.0, and 1.5% by volume fraction.While the incorporation of SF does not yield a substantial improvement in compressive strength, it substantially enhances fracture tensile strength and flexural behavior.The load-displacement graph demonstrated that the incorporation of steel fibers into geopolymer composites increased their fracture toughness, resulting in a higher maximal load capacity.The findings suggest that the incorporation of RCA into SCGPC reduces its flexural behavior, splitting and compressive tensile strengths, and durability, particularly under peak load, deflection, and load.Furthermore, it is observed that RCA negatively synergize with respect to compressive and fracturing tensile strength.However, SF exhibit a significant positive synergy in terms of flexural properties.
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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) | 0.001 | 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".