Nondestructive Fatigue Damage Analysis of a Thin Asphalt Concrete Course Using the Wavelet Correlation Method
Bibliographic record
Abstract
Abstract Stress wave analysis is employed herein as a nondestructive monitoring tool to assess the level of fatigue damage in a thin asphalt concrete (AC) overlay. A frequency-dependent cross-correlation procedure is developed to specify a stress wave at a desired frequency by using a wavelet kernel. This procedure is referred to as the wavelet correlation method (WCM). Once synthetic surface waves are constructed and subjected to simulated disturbances, such as structural damage or nearby frequencies, their phase velocities are computed using the WCM with over 96 % accuracy. The generated stress waves are periodically processed, while laboratory hot-mix asphalt pavements are trafficked by the third-scale model mobile loading simulator. The dispersion curves are then analyzed to validate that a wave of 16 kHz travels mainly within a 40∼60 mm thickness of a surface layer. Fatigue damage levels are quantified at intervals by the phase velocity that represents the AC elastic modulus. Microdamage healing of the AC during rest periods is then indexed and corrected by shifting the damage progress profile. Consequently, an early reduction in phase velocity, which is caused by microcracking, can be visually observed in the surface cracking once the phase velocity is reduced to about 50 % of the initial value regardless of pavement density and aggregate gradation. Thus, the WCM allows the optimal timing and scheduling of the preservation construction of a thin AC overlay by indicating the critical microdamage stage immediately prior to the visual evidence of surface cracking.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".