Scaling relationships for strip fibre–reinforced aggregates
Bibliographic record
Abstract
Previous research on random fibre-reinforced granular materials has shown that the relative dimensions of the grains and fibres significantly affect the macromechanical behaviour of the mixture. However, quantitative data are scarce and most previous work has focused on fine to medium sands, leaving uncertainties regarding the applicability of current knowledge to larger size aggregates such as railway ballast. In this paper, triaxial test data on 1/3 and 1/5 scale railway ballast are used to develop scaling relationships for the size and quantity of fibres needed to achieve the same reinforcing effect in granular materials of differing grain size. It is shown that, to maintain consistency across scales, fibre content should be quantified as a numerical (i.e., number of fibres per grain) rather than a volumetric ratio. It is further shown that increasing the fibre length increases the resistance of the mixture to deviator stress if the fibres are wide enough; and that provided an allowance is made for the effect of fibre tension, the changes in the stress–strain–strength behaviour of the granular matrix resulting from the changes in void ratio associated with the addition of the fibres are consistent with conventional soil mechanics theory across scales.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".