State parameter predictions based on cone penetration test simulated with MPM: an application to tailing deposits
Bibliographic record
Abstract
The ‘state parameter’, which compares the current void ratio with the critical state void ratio, plays a crucial role in quantifying sandy soil behaviour. In situ methods, such as cone penetration tests (CPTs), can quantify the mechanical state of sand. However, establishing a direct relationship between cone resistance and the state parameter requires a complex back-analysis of the processes occurring in the soil during the test. Currently, a cavity expansion solution is being used to relate the state parameter to the cone resistance, necessitating the use of a calibrated scaling equation. In this study, 600 material point method CPT simulations are performed – which employ the critical state NorSand model – to derive a direct predictive equation for estimating the state parameter from CPTs. This eliminates the need for a scaling equation. The predictive equation computes cone resistance as a function of the NorSand parameters and the state parameter of the soil. The accuracy of this predictive equation is subsequently evaluated by comparing the results against the chamber test data of tailings deposits, showing promising performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".