Bibliographic record
Abstract
Abstract The most widely used methods for the in situ investigation of the mechanical characteristics of soil are field penetration tests such as the standard and cone penetration tests, which apply several empirical correlations. Many geotechnical researchers have tried to improve the empirical correlations by analyzing comparisons of field penetration tests and laboratory simulations, as well as virtual numerical simulations. However, geotechnical parameters such as relative density, void ratio range, grain size distribution, angularity, mean particle size, uniformity coefficient, effective overburden stress, and mean confinement stress have interlocking effects. Consequently, the use of calibration chambers, which can simulate ideal conditions for the soil in the laboratory, are essential. The authors have developed an axisymmetric innovative simulator system, not only to perform parametric studies on the aforementioned parameters but also to simulate a range of field conditions under substantially controlled boundary conditions. This article also presents results for a series of calibration tests to prove its applicability on nonplastic silts, sands, and granular materials. Repetitive tests, under identical testing circumstances, verified the accuracy, efficiency, and durability of the system. The current work also correlates with the results of comparative studies to investigate the effectiveness of the simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".