Laboratory-scale characterization of saturated soil samples through ultrasonic techniques
Bibliographic record
Abstract
The propagation of poroelastic waves in a soil specimen is dependent on the physical and mechanical properties of the soil. In the geotechnical practice, such properties are obtained through in-situ geotechnical testings or element soil testings in the laboratory. These methods require almost advanced equipment and both testing and sample preparation may be expensive and time-consuming. This paper aims to present an algorithm for a laboratory-scale ultrasonic non-destructive testing to determine the physical and mechanical properties of saturated soil samples based on the distribution of stress waves. The ultrasonic setup, in comparison to most conventional soil lab testing equipment, is low-cost and non-invasive such that it reduces the soil disturbance. For this purpose, a poro-elastodynamic forward solver and differential evolution global optimization algorithm were applied to characterize the porosity, density, and other mechanical properties for a soil column. The forward solver was developed based on a semi-analytical solution which does not require intensive computational efforts encountered in standard numerical techniques such as the finite element method. It was concluded that the proposed high-frequency ultrasonic technique characterizes effectively the saturated soil samples based on the output stress wave measured by the receiver. This development makes geotechnical investigations time-efficient and cost-effective, and as such more suited to applications in remote areas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".