Shear strength of fibre reinforced cemented Toyoura sand
Bibliographic record
Abstract
<abstract> <p>A series of consolidated drained and undrained tests are conducted on unreinforced, fibre, cement, and fibre reinforced cemented Toyoura sand specimens with varying relative densities. Three different types of materials e.g., Toyoura sand, polyvinyl alcohol (PVA) fibres, and ordinary Portland cement (OPC) are employed in this study. Specimens in dimensions of 50 mm in diameter and height of 100 mm are prepared in a polyvinyl chloride (PVC) mold to a target dry density value, $ {\rho }_{d} $ = 1.40 g/cm<sup>3</sup> (Dr = 20%) and $ {\rho }_{d} $ = 1.489 g/cm<sup>3</sup> (Dr = 60%) of Toyoura sand using under-compaction moist tamping technique. Fibre reinforced cemented Toyoura sand samples were prepared with 10% moisture content by weight of sand-fibre-cement mixtures. The results on density variation shows that due to a better contact between sand-fibre interaction or sand-cement-fibre bonding and interaction for the denser specimens, a greater increase in shear strength is observed. However, the general effectiveness of fibre and cement additives alone and when mixed together also enhances the strength of unreinforced specimens for loose conditions based on the variation of fibre and cement contents. The results and findings in the current study can be used for the construction of economical and sustainable geotechnical infrastructures.</p> </abstract>
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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 | 0.001 | 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) | 0.003 | 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".