CPT-Based Shear Wave Velocity Correlation Model for Soft Soils with Graphical Assessment
Bibliographic record
Abstract
Shear wave velocity is a key parameter for evaluating the mechanical properties of soils, and direct measurement is technically demanding and costly. Realizing rapid prediction by establishing correlations between other parameters and shear wave velocity is an economical solution. Combined with the drilling data from 12 different areas of Shanghai’s soft ground layer, the regression models of shear wave velocity Vs and cone penetration resistance Ps versus burial depth H were established, and the new models were assessed by the existing regression models, graphical analyses, and statistical assessment methods. The results show that the existing regression models between shear wave velocity and cone penetration resistance cannot effectively predict the shear wave velocity of soft soil layers in Shanghai; the shear wave velocity of soft soil layers is closely related to cone penetration resistance and burial depth; and the newly established regression model can more accurately calculate the shear wave velocity of soft soil layers in Shanghai. This study provides an economical and effective solution for the rapid prediction and engineering application of shear wave velocity in soft soil layers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".