Measuring shear wave velocity of granular material using the piezoelectric ring-actuator technique (P-RAT)
Bibliographic record
Abstract
A precise evaluation of shear wave velocity, V s , is a crucial issue in the design of foundations subjected to dynamic loading, liquefaction evaluation, and soil improvement control. Laboratory techniques such as resonant column (RC) and bender element (BE) have been developed over the years to measure V s . At low strain (γ < 10 −3 ), techniques based on piezoelectric elements (e.g., BE) can be considered superior to RC, as they can be used in conventional geotechnical devices (e.g., triaxial, oedometer, direct simple shear, etc.). However, it is a difficult task to verify that the obtained V s values are correct and accurate, as there are several difficulties associated with these methods, including the mixed radiation of both primary and shear waves, near-field effects, boundary effects, and uncertain detection of first arrivals. This paper presents the use of a new technique to measure V s in granular material, called the piezoelectric ring-actuator technique (P-RAT), developed at the Université de Sherbrooke. The paper also provides a detailed description of a unique interpretation method of the signals produced from this technique to minimize the difficulties associated with other techniques. The P-RAT has been incorporated into the well-known oedometer cell to measure the V s of Péribonka sand through a series of oedometric tests, and the obtained results have been detailed, analyzed, and discussed in light of the basic state of knowledge of V s and factors affecting it. Particular emphasis is also placed on the validation of the accuracy of the P-RAT by means of reliable experimental measurements available in literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".