Correlation between Voids Ratio and Characteristic Responses to Ultrasonic Pulses Through Sandy Soils
Bibliographic record
Abstract
The microporous structure of granular soils that provides important information such as shear strength, compressibility, and hydraulic conductivity, is directly influenced by the void ratio. Therefore, a quick identification of the void ratio, by a non-destructively way and in situ becomes an interesting practice. Ultrasound has been successfully used for ceramic materials, wood, concrete and rocks. When dealing with soils, great efforts are made to understand its behavior and characteristics through wave propagation velocity. However, does still have a lack of discussions about the ultrasonic wave properties. In this paper, a study analysing 156 saturated samples of sands by ultrasound for 3 median particle size, with void ratio ranging from 0.5 to 1.1. The ultrasonic wave measurement was performed using the transmit and receiveand technique, with 50 kHz transducers. The study demonstrates a possibility of identifying transition zones between sample’s materials composition. Considering the same dominant medium, a correlation was observed among the void ratio and the maximum amplitude, the damping coefficient, dominant frequency and ultrasonic pulse speed. There is also the identification of characteristic frequencies for these media. Therefore, the novelties of the present study are mainly the development of a feasible technique to investigate void ratio of granular saturated soils using direct measurements of the ultrasonic wave characteristics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".