Bibliographic record
Abstract
Calibration chambers are employed to reproduce field conditions in laboratory cone penetration tests (CPTs). However, their measurements are often affected by the limited size of the calibration chamber and the imposed boundary condition. Hence, the chamber size effect needs to be considered in predicting field performance or verifying and establishing new correlations between cone resistance and soil properties from CPT calibration chamber results. The effect of chamber boundary condition is examined in this study through both numerical simulations and laboratory CPT calibration chamber tests. For this purpose, a series of discrete-element simulations were performed and compared with experimental calibration chamber tests to investigate the influence of the boundary condition on cone tip (qc) and sleeve frictional (fs) resistances. The numerical simulations were further used to study the effects of particle size, sample height, relative density, and effective overburden pressure on qc and boundary effects. The results indicated that qc in calibration chamber tests on loose to medium-dense sands and those carried out with a rigid boundary would be closer to the field condition and would require little compensation for the limited effect of chamber size. Based on these findings, specific correction factors are proposed to better estimate the free field penetration resistances.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".