Evaluation of state parameter interpretation methods using CPT calibration chamber data
Bibliographic record
Abstract
The cone penetration test (CPT) is widely used to determine the in situ state parameter of soils because it provides continuous data and excellent repeatability at a relatively low cost. Accurate interpretation of the state parameter from CPT is the basis for evaluating the strength of granular soils, including assessing liquefaction susceptibility in important structures such as tailings storage facilities. A few interpretation methods are used in practice. They use two different overburden stress normalisation schemes on tip resistance. These methods vary in how much information they utilise to differentiate among soils. This paper evaluates these methods by applying them to an extensive database of calibration chamber tests. Then, the state parameter interpreted by each method is compared with that determined from laboratory data. The database includes manufactured sands, natural sands, and clean sand tailings. The soils were selected such that both calibration chamber testing and triaxial compression data were available from the literature. This evaluation serves as a minimum requirement for applying these methods in engineering projects, especially those dealing with challenging soils such as fines-rich tailings. This study suggests that methods that account for soil properties and in situ horizontal stresses perform better than those that do not.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".