Ultrasonic inspection of asphalt pavements to assess longitudinal joints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Longitudinal joints existing in between the lanes of asphalt pavements may initiate deterioration. Traditionally, core density, deflection, and nuclear density tests are used for the quality control. However, such techniques may not suit to the surface at the joints to assess their condition. Alternatively, the ultrasonic surface wave (USW) methods have the potential to both assess the longitudinal joints and estimate the pavement thickness at the same time. In this study, the USW are investigated on two lab-scale asphalt slabs (one laboratory prepared, and the other is cut from an as-built pavement) and on an in-service asphalt pavement to develop an ultrasound-based assessment methodology. Initially, an empirical compaction model is developed to produce the custom-size slab with the desired air-void profile to mimic a pavement with joint. Then, a variety of coupling systems between the pavement and the ultrasonic transducers are trialed to determine the optimum one. The recorded data are processed to determine the dispersion in velocity and the attenuation, which are then interpreted to estimate the pavement thickness and assess the joint quality, respectively. The dispersion curve is found capable of determining the pavement thickness with a precision of 1 cm, while the attenuation curve is observed to be affected by the transducer configuration excessively. Therefore, a normalisation technique, named the Fourier transmission coefficient (FTC), is implemented to reduce the undesired variability caused by the transducer coupling and type. Finally, it is demonstrated on an as-built pavement that the FTC has promising potential to detect, and hence evaluate the quality of longitudinal joints.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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