Significance and evaluation of Poisson's ratio in Rayleigh wave testing
Bibliographic record
Abstract
Important progress has been made in the past 20 years in the use of surface-wave testing in soil characterization. However, the effect of Poisson's ratio on the construction of the theoretical dispersion relationships, associated with the inversion process, has not received enough attention and remains poorly documented. Five ideal profiles with different degrees of variation of shear-wave velocity with depth and three published case records are considered in this paper to study the effect of Poisson's ratio on Rayleigh wave phase velocities. The effect of the variation of Poisson's ratio on the evaluation of shear-wave velocity profiles (Vs) is also examined. Poisson's ratio is generally assumed in surface-wave testing, and therefore the paper also examines the possibility of evaluating its value using a multi-mode inversion process. The results of surface-wave testing obtained at two experimental sites are then used to illustrate the potential of surface-wave testing to evaluate the Poisson's ratio profile in addition to the Vsprofile. The impact of Poisson's ratio in Rayleigh wave testing is shown to be significantly more important than previously demonstrated. The error resulting from Poisson's ratio does not depend solely on the magnitude of the inaccuracy. A multi-mode inversion process is shown to be a useful tool to determine the Poisson's ratio profile, leading to a more accurate soil characterization.
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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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".