Sensor and Dimensions Effects in Ultrasonic Pulse Velocity Measurements in Mortar Specimens
Bibliographic record
Abstract
Ultrasonic Pulse Velocity (UPV) method is a very popular technique used in Non-Destructive Testing (NDT) in Civil Engineering. Major benefit of the method is its simplicity. UPV uses the concept of measuring time of a first arrival of ultrasonic wave from one side of the specimen to another. Moreover, UPV is an ASTM standard test method for concrete specimens. The standard specifies the applications of UPV as: assessment of relative quality of concrete, presence of imperfections (i.e. voids, cracks, and the effectiveness of its repairs). UPV can be also applied to monitoring changes in the condition of a specimen. In spite of an easiness of the method the obtained results highly depend on the transducers used, the coupling quality, and the specimen dimensions. In this article the authors focus on the sensor and the dimensions effects. The results for UPV tests on 9 mortar specimens of different heights and diameters are presented. The specimens are tested with 54 kHz and 850 kHz resonant frequency (fc) transducers and the state-of-the-art laser vibrometer (response measurements). The authors discuss the laser vibrometer readings and the influence of specimens’ dimensions on the measured pulse velocities. Practical recommendations for the minimal dimensions of the test object in order to minimize the error in the UPV tests are proposed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".