Cone penetration test (CPT)-based soil behaviour type (SBT) classification system — an update
Bibliographic record
Abstract
A soil classification system is used to group soils according to shared qualities or characteristics based on simple cost-effective tests. The most common soil classification systems used in geotechnical engineering are based on physical (textural) characteristics such as grain size and plasticity. Ideally, geotechnical engineers would also like to classify soils based on behaviour characteristics that have a strong link to fundamental in situ behaviour. However, existing textural-based classification systems have a weak link to in situ behaviour, since they are measured on disturbed and remolded samples. The cone penetration test (CPT) has been gaining in popularity for site investigations due to the cost-effective, rapid, continuous, and reliable measurements. The most common CPT-based classification systems are based on behaviour characteristics and are often referred to as a soil behaviour type (SBT) classification. However, some confusion exists, since most CPT-based SBT classification systems use textural-based descriptions, such as sand and clay. This paper presents an update of popular CPT-based SBT classification systems to use behaviour-based descriptions. The update includes a method to identify the existence of microstructure in soils, and examples are used to illustrate the advantages and limitations of such a system.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.011 |
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".