Ground Penetrating Radar Quality Assurance Characterization of Asphaltic Concrete Surfacing
Bibliographic record
Abstract
Saskatchewan Ministry of Highways and Infrastructure commissioned a ground penetrating radar (GPR) survey to develop a non-destructive methodology for performing post construction quality assurance characterization of a newly upgraded flexible pavement structure. The survey employed multiple passes of GPR to determine conventional hot mix asphalt concrete (HMAC) surfacing quality assurance measures including layer variability thickness and density. The GPR survey found that 21 percent of the southbound lane and 14 percent of the northbound lane showed low, moderate, or high severity surfacing variability. It was also determined that more asphalt mat variability was present towards the outside of both lanes. Based on the results of this study, GPR was found to provide a measure of HMAC surface irregularity, as well as the severity of the surface variability. The pilot GPR analysis correlated well with the visual condition survey of the surface. As well, the GPR measured HMAC layer thicknesses were validated as they correlated well with retrieved HMAC core thicknesses. However, the GPR did not provide an adequate correlation to surface density. In summary, this research demonstrates that GPR may be a valuable non-destructive measurement tool to provide quality assurance measures of mat thickness and variability of HMAC surfaces in Saskatchewan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".