Proof-of-Concept Shear Wave Velocity Measurements Using an Unmanned Autonomous Aerial Vehicle
Bibliographic record
Abstract
The feasibility of using an unmanned autonomous aerial vehicle (UAAV) for the performance of shear wave velocity measurements is investigated. Proof-of-concept tests using an UAAV developed at the University of Michigan were performed at two sites, and the results are presented. Tests were performed first at an indoor sand pit facility at the University of Michigan that was filled with Ottawa sand to a depth of 3.7 m (12 ft). A second test was performed outdoors, at a stiffer soil site near campus. The multichannel analysis of surface waves method (MASW) was implemented using an array of 16 geophones. Conventional testing was performed using a hammer as a source. Additional testing was performed using as a source a 0.18 kgr (0.4 lb) mass that was dropped by the UAAV from an elevation of approximately 3 m (9.5 ft). Comparison of the results is made on the basis of the measured dispersion curves and the interpreted shear wave velocity profiles. The results from the two techniques were very similar, indicating that this technology has promise as a site characterization tool. A wireless sensor was also placed adjacent to a wired sensor to evaluate the feasibility of using a wireless sensor network for the performance of these tests. It is shown that the signals of the two sensors were essentially identical.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".