Laboratory scale column penetration tests for deep mixing purposes
Bibliographic record
Abstract
Strength verification of dry deep mixed columns is almost exclusively performed by column penetration tests (KPS) using a probe with two wings shearing into the column. Guidelines specify a constant bearing capacity factor ( NKPS) of 10 to determine the shear strength of the column. This factor has, however, undergone little research, and there are considerable research gaps. Results from laboratory scale tests are presented herein, where cement-improved kaolin columns with three different strengths were tested with KPS and cone penetration tests (CPT). The results showed NKPS ranging from 7.3 to 7.8 at low degrees of confinement around the columns, up to 15.4–16.5 at high degrees of confinement. The column strength did not significantly affect NKPS. The results further indicates that extraction to penetration ratio can be used to predict NKPS. Equivalent bearing capacity factors for CPT were 5.4–7.8 for tests only performed in low degrees of confinement. The findings present new knowledge that degree of confinement is crucial for NKPS, which has important practical implications, particularly that current guidelines oversimplify and might yield unconservative column strengths with increased risk of failure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".