Effects of a Fracture on Ultrasonic Wave Velocity and Attenuation in a Homogeneous Medium
Bibliographic record
Abstract
Abstract Nondestructive ultrasonic testing is commonly used to assess damage in infrastructure mostly based on elastic wave velocity. This study focuses on understanding the effects of a thin fracture not only on ultrasonic elastic wave velocity but also on attenuation. Experiments are performed to quantitatively assess the effects of a thin fracture within polymethylmethacrylate (PMMA) specimens. Wave velocity and attenuation are measured across the width of these homogeneous specimens using the ultrasonic pulse velocity method. Seventeen specimens are tested for three different conditions (intact, with a hole, and with a fracture) for two different thicknesses. First, specimens made of two PMMA blocks with an intact fused interface are tested; then, specimens with a small hole (created for generating stress concentration) perpendicular to the interface and milled ends are tested; and, finally, specimens with an induced fracture at the fused interface are tested. Four additional specimens, two with fused (but weak) interfaces between blocks and two solid blocks, are tested during fracture growth under uniaxial strain-controlled test conditions. In fact, wave attenuation can cause the first arrival to be undetected and overestimated by up to 10 %. This error in the selection of the first arrival could be misinterpreted as a change in wave velocity when fractures are present in the material. Although wave velocity shows marginal reduction, less than 4 %, when a thin fracture is present, wave energy attenuates by up to 60 %. This work demonstrates quantitatively that wave attenuation measurements from selected frequency bands in the Fourier spectra can be used to identify the presence of thin fractures using ultrasonic testing.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".