Field Condition Assessment of Longitudinal Joints in Asphalt Pavements Using Seismic Wave Technology
Bibliographic record
Abstract
Poor-quality longitudinal construction joints often contribute to the poor performance of hot mix asphalt (HMA) pavements. Traditionally, the longitudinal construction joints are evaluated in terms of in-situ density measurements obtained through coring at five different locations across the joint. This approach is destructive, time consuming which limits the implementation of the quality assurance and quality control (QA/QC) plan to ensure the construction of good quality longitudinal joints in asphalt pavements. To address this problem, an innovative non destructive technique (NDT) for condition assessment of the longitudinal construction joints in asphalt pavements has been developed at the University of Waterloo in collaboration with the Ministry of Transportation, Ontario. This method involves the use of ultrasonic surface waves to assess the relative condition of the longitudinal joints in comparison to the condition of the adjacent good quality joint-free asphalt pavement surface. In this approach, novel experimental and signal processing techniques are used to minimize the variability associated with unknown limitations of wave source and receivers, wave path characteristics, and the effects of source/receiver coupling used for measuring wave attenuation across the joints. Based on the findings of the laboratory study, a field testing protocol was developed involving two types of NDT tests. A pilot field study was conducted to evaluate the suitability of the test protocol developed for field applications. Presented in this paper are the results of the pilot study which indicates that the proposed NDT test method is a viable and effective alternative to density measurements for field assessment of the longitudinal joints in asphalt pavements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".