Time–Frequency Domain Analysis of Asphalt Longitudinal Strain
Bibliographic record
Abstract
The application of time–frequency domain analysis methods, such as continuous wavelet transform (CWT) and short-time Fourier transform (STFT), is evaluated in the extraction of the dominant frequency from asphalt longitudinal strain signals. The pavement response data collected at the fully instrumented Integrated Road Research Facility in Edmonton, Alberta, Canada, was used in this study. Promising results were achieved when CWT and STFT were used to determine the dominant frequency of the measured longitudinal strain at the bottom of the asphalt layer at different vehicular speeds. The dominant frequencies obtained from these methods were compared with those from conventional time-to-frequency conversion methods. Results showed that a frequency calculation that used the inverse of tensile pulse duration led to noticeably larger frequencies compared with those associated with the CWT and STFT methods. The accuracy of the determined frequencies and the corresponding moduli of hot-mix asphalt were evaluated by predicting strains with the KENPAVE program. This analysis showed the advantage of using time–frequency domain methods because they led to more reasonable agreements between the measured and predicted responses. Finally, the impact of frequency calculation methods on the potential fatigue cracking life was assessed by taking the estimated moduli and strains of hot-mix asphalt into account. It was found that the fatigue cracking life can be overestimated by almost 45% when frequency is considered as a reciprocal of pulse duration compared with when prediction is based on time–frequency domain methods.
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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 | 0.001 | 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) | 0.001 | 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".