Bibliographic record
Abstract
The Cone Penetration Test has quickly and rightfully claimed its position as a popular site investigation tool in geotechnical engineering. Like all penetration tests, CPT results need to be interpreted into engineering parameters. In granular soils, the state parameter, which is the key driver of soil behaviour is explicitly the target of interpretation in applications such as liquefaction assessment in tailings. Any other application that depends on density or dilatancy implicitly depends on the state parameter. Existing empirical methods are almost entirely based on clean sands and completely or partially miss the significant dependency of the interpretation on intrinsic properties of soils. Use of calibration chamber tests to create soil-specific correlations are prohibitively expensive, and numerical models of the CPT have had limited success due to the complicated nature of the deep penetration problem.This dissertation presents a full model of the Cone Penetration under drained conditions using a critical state based constitutive model. The constitutive model, NorSand, was implemented in a Finite Element platform (ABAQUS) capable of handling large deformations. The real geometry of the CPT was modelled with contact elements and analyses were performed utilizing an Arbitrary Lagrangian Eulerian formulation. To create a predictive tool, calibration of soil behaviour and the contact friction were done in a completely forward manner. Drained triaxial compression tests were analyzed to investigate the level of potential error in calibration due to test imperfections. More than 300 calibration chamber tests were then modelled for validation. A parametric study was done to create a practice-ready interpretation method for obtaining the in-situ state parameter from CPT tip resistance. The method is the first well-validated technique that can generate soil-specific correlations beyond clean sands given its mechanistic framework.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".