Development of global correlation models between in situ stress-normalized shear wave velocity and soil unit weight for plastic soils
Bibliographic record
Abstract
Shear wave velocity (Vs) in geo-materials is strongly dependent on factors such as stress state, void ratio, and soil structure. Stress-dependency and void-ratio dependency can be represented by the equations [Formula: see text] and Vs = a(e)b (where α and a are material constants; exponents β and b represent the sensitivity of stress and the void dependent effect, respectively; [Formula: see text] is effective confining stress; e is void ratio), respectively. To consider the effect of soil disturbance and stress relief in geo-materials, shear wave velocity is often required to be normalized by adopting the site-specific model parameters (β or b). Based on a special in situ database compiled from 156 well-documented test sites that include various geo-materials, this study presents (i) the apparent relationships of the model parameters α and β for all soil and rock materials as well as a and b for all soil materials, (ii) new global correlations between soil unit weight and two types of stress-normalized shear wave velocities (Vs1 and Vsn), instead of the conventional Vs – soil unit weight relationship for clays, and (iii) the best-fitted multi-regression models between soil unit weight and site-specifically normalized shear wave velocity as well as the plasticity index for plastic soils. Moreover, this study presents the importance of site-specific stress normalization (Vsn) in creating a better correlation model. The proposed relationships offer first-order assessments of soil unit weight within the ranges of available data, which are also approximately guided by a hyperbolic unit weight model with depth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".