Effect of Overconsolidation on K in Centrifuge Models Using CPT and Tactile Pressure Sensor
Bibliographic record
Abstract
Abstract Assessing the state of stress in soil in the free field, as defined by the vertical and lateral earth pressures, is important in some geotechnical problems such as the design of underground structures and estimation of soil liquefaction potential. Unlike the vertical earth pressure, several factors can influence the lateral earth pressure, particularly overconsolidation. This is typically accounted for by relating the coefficient of lateral earth pressure at rest, K0, with the estimated overconsolidation ratio (OCR). The corresponding relations available in the literature are related to the K0 occurring for a given OCR during unloading, and do not take into account the unloading–reloading effect that may be present in centrifuge tests. In this paper, the relationship between the coefficient of lateral earth pressure at rest, K0, and overconsolidation ratio, OCR, is investigated for the whole loading–unloading–reloading condition. Four centrifuge model experiments were conducted on Ottawa F#55 sand. Two of the models were tested using a tactile pressure sensor to measure K0 and the other two models were tested using a miniature cone penetration test (CPT) system. The paper concludes with recommendations for centrifuge operation when testing overconsolidated sand in the centrifuge.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".