Novel Analysis of Ultrasonic Pulse Propagation Tests for Characterization of Asphalt Concrete
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT The need for better pavement material characterization has become the focus of several studies lately. Better characterization is necessary to promote mechanistic-based pavement analysis methods. The current availability of nondestructive testing methods offers significant benefits to improve the accuracy of conventional testing methods as well as the development of fast quality control tools. To this end, a new analysis of the ultrasonic pulse propagation tests (UPPT) in asphalt concrete mixes can provide a more complete evaluation of viscoelastic properties of these materials by providing results at a wider range of temperatures and loading frequencies. UPPTs can also be used as a complementary tool to improve conventional asphalt concrete characterization techniques. This study evaluates the use of UPPT for the characterization of asphalt concrete mixes using 2 main excitation frequencies of 54 and 150 kHz. Measurements are performed at 5 temperatures ranging from −11°C up to 54°C. The nondestructive nature of the UPPT allowed for the use of the same specimens for uniaxial dynamic modulus tests. These tests were performed using six different loading frequencies at each of the five selected temperatures. The results indicate that the accuracy of the modulus master curves can indeed be improved by combining the results from the UPPTs and the conventional dynamic modulus tests. The results also underline the importance of accounting for the temperature-dependent Poisson’s ratio of asphalt mixes when calculating the norm of complex modulus from P-wave ultrasonic measurements. Furthermore, to the traditional time-of-light analysis, the results show a strong correlation between the phase angle from conventional mechanical testing and the attenuation parameter from ultrasonic waves at different temperatures.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it