Condition assessment of longitudinal pavement joints using ultrasonic surface waves
Bibliographic record
Abstract
Poor-quality longitudinal joints can cause premature failure on asphalt pavements; they are characterized by exhibiting low density and high permeability. These conditions generate surface distresses such as longitudinal cracking. Previous ultrasonic methods for condition assessment, e.g., Fourier and wavelet transmission coefficient, require user input, making the automatic data processing difficult. Furthermore, the coupling between ultrasonic transducers and the asphalt surface is not practical for testing in-service roads. This paper presents a new data analysis technique and the results of an experimental program for the condition assessment of longitudinal asphalt joints using ultrasonic surface waves. A new coupling system between the ultrasonic transducers and the asphalt surface is presented. The new data processing technique reduces user input and increases testing reliability. The new coupling system uses polyurethane foam and calibrated weights to provide a spring action on the transducers. The proposed data analysis technique, called instantaneous transmission coefficient (ITC), is based on the evaluation of instantaneous frequencies and damping ratios. The main advantage of the new procedure is that it can be performed automatically, reducing user input and increasing test repeatability. A laboratory scale asphalt slab is used to show the potential of the new methodology and coupling system. Laboratory validation results show good agreement between the new ITC method and previous methods, and a significant reduction in testing time, while improving test reliability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".