High-pressure compressibility and shear strength data for soils
Bibliographic record
Abstract
Soil behaviour is often an important consideration in the design of protective systems for blast and impact threats, as the properties of a soil can greatly affect the impulse generated from buried explosive devices, or the ability of a soil-filled structure to resist ballistic threats. Numerical modelling of these events often relies on extrapolation from low-pressure experiments. To develop soil models that remain accurate at very high pressures there is a need for data on soil behaviour under these extreme conditions. This paper demonstrates the use of a high-pressure multi-axial test apparatus to provide compressibility and shear strength data for four dry sandy soils. One-dimensional compression experiments were performed to axial stresses of 800 MPa, where the effects of particle-size distribution were observed with respect to compressibility and bulk unloading modulus. Each soil followed a bilinear normal compression line (NCL): more uniform soils initially had higher compression indices, but all four NCLs began to converge at void ratios below e ≈ 0.3. The failure surface of a sand was characterized to mean effective stress [Formula: see text] > 400 MPa using reduced triaxial compression experiments, removing the need to rely on extrapolation from low-pressure data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 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".