Combining Portable Falling Weight Deflectometer and Surface Wave Measurements for Evaluation of Longitudinal Joints in Asphalt Pavements
Bibliographic record
Abstract
Longitudinal joints in asphalt pavements typically have lower densities than the interior portion of the mat. Therefore, they tend to exhibit surface distresses such as cracking and raveling more rapidly. The objective of this study is to evaluate the ability of nondestructive testing (NDT) for assessing the relative condition of a longitudinal joint. NDT has been significantly developed for pavement evaluation during the past decade. Deflection and surface wave methods are the most commonly used for pavement evaluation. The portable falling weight deflectometer (PFWD) is increasingly used in quality control and quality assurance to provide rapid determination of the equivalent surface elastic modulus. However, the multichannel analysis of the surface waves (MASW) method consists of using ultrasonic transducers to measure surface waves traveling through the pavement and invert for the elastic modulus of different layers. This study presents results from both deflection and seismic methods to assess the quality of longitudinal joints. Both methods are performed at the same locations of the centerline of a test track. Pavement deflection is also measured on the wheelpath for comparison with previous data. Across the joint, changes smaller than 7% were observed in deflection values. These preliminary results cannot be directly related to joint quality because the contribution of sublayers to the modulus measured with the PFWD needs to be estimated. MASW measurements showed promising results for evaluating the attenuation of surface waves due to the joint. Further work is required to improve the coupling between the transducers and asphalt surface.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".