Use of laboratory shear wave velocity measurement to enhance in-situ data
Bibliographic record
Abstract
Abstract Shear wave velocity (Vs) plays a central role in soil dynamics and is widely used in seismic site classification, liquefaction assessment, and earthquake stability analysis. One of its distinguishing features is that it directly relates to small-strain shear modulus (G = ρVs2), making it the only mechanical property that can be consistently measured in both field and laboratory settings. Field measurements of Vs are particularly valuable because they capture soil stiffness in its natural, undisturbed state, offering insight into in-situ density conditions. Despite its relevance, Vs remains underutilized in geotechnical practice. Given its sensitivity to factors such as soil density, stress conditions, and preloading history, accurate measurement of Vs can provide a robust and reliable geotechnical parameter. Several studies have reported that shear wave velocities obtained from laboratory tests tend to be lower than those measured in the field. This discrepancy is commonly attributed to disturbances caused by the sample extraction process. Such disturbance effects are not limited to Vs measurements; they also impact other geotechnical properties assessed in the lab, such as undrained shear strength and pre-consolidation pressure. To address this issue, it is essential not only to work with high-quality clay specimens but also to develop strategies for quantifying and correcting sampling-induced alterations. This study introduces a novel method to evaluate the extent of disturbance resulting from sampling and specimen handling. It further proposes a correction approach for laboratory-derived stiffness parameters. These corrected values are then benchmarked against in-situ measurements obtained under undisturbed conditions. Finally, the adjusted laboratory data are integrated to enhance the interpretation of field measurements in stres-strain analyses.
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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 | 0.003 |
| 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 | 0.000 |
| 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".