Shear strength characterisation of biogenic, highly crushable coral-bearing composite soil through large-scale multi-reversal direct shear test
Bibliographic record
Abstract
With the dominant presence of biogenic coral gravels, the shear strength behaviour of coral gravel soil differs significantly from that of terrestrial soils. However, much less is known about the shear strength behaviour of this soil, especially when subjected to large shear displacements. This study performs large-scale direct shear testing with multi-reversal shearing, initiating substantial shear displacements. The effects of vertical stress and shear displacement on shear stress development, shear strength parameters, and volumetric deformation characteristics are revealed. In addition, the specimen in this study is sufficiently large, thus allowing for a spatial distribution analysis of particle crushing levels. It is revealed that the conventional evaluation method of particle crushing level introduces an underestimation of approximately 40% and that particle crushing is much more intense within the shear band than in other regions. Another important contribution of this study over previous ones is that the evolution of soil microstructure with straining is established. Continuous shearing alters the soil microstructure in two ways: (i) initiating and propagating fissures, breaking particles, and (ii) rearranging soil particles, which ultimately leads to the residual state. This study provides a fresh data set for coral-bearing soil and new understandings of its mechanical behaviour.
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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) | 0.001 | 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".