Static and dynamic behaviour of compacted sand and clay: Comparison between measurements in Triaxial and Oedometric test systems
Bibliographic record
Abstract
ABSTRACT In rock mechanics and rock physics, like in many other branches of research, it is important to compare results obtained in different kinds of apparatus that are meant to measure the same properties. Differences may in general be due to differences in samples, or in test procedures. Here we compare uniaxial compaction experiments in oedometric and triaxial tests systems, using brine‐saturated samples made from pure kaolinite or from Ottawa sand. Small differences in sample manufacturing or in initial loading of the specimens were found to cause significant differences in static behaviour and in ultrasonic velocities during the tests. The influence of differences in sample geometry (wide and thin samples in the oedometer versus long and slim samples in the triaxial set‐up) and the influence of different boundary conditions caused by the confining medium (steel in the oedometer, thin soft sleeve in the triaxial system) were studied, amongst others with the use of discrete particle modelling. Although the boundary conditions may have an influence, the most significant sources of discrepancy in our experiments were associated with the manufacturing and preparation of the samples to be tested. The test data show that the drained static compaction modulus for sand is close to its dynamic counterpart, while for kaolinite, the dynamic modulus is significantly larger than the static one.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".