RAPID IN-SITU SHEAR TESTING OF ASPHALT PAVEMENTS FOR RUNWAY CONSTRUCTION QUALITY CONTROL AND ASSURANCE By:
Bibliographic record
Abstract
In Canada, almost all airport runways have been constructed or rehabilitated with hot mix asphalt concrete based on cost, performance and speed of construction considerations. As with highway pavements, permanent deformation through heavy (aircraft) traffic then becomes a major concern to airport authorities, who cannot afford premature failure of runway pavements. In cooperation with the United States Transportation Research Board and the Ontario Ministry of Transportation, researchers at Carleton University in Ottawa, Canada have developed the In-Situ Shear Stiffness Test (InSiSST ™ ) – a new test facility for measuring shear properties of compacted asphalt concrete layers in the field. The ability to rapidly measure in-situ pavement properties immediately after construction is particularly beneficial to airport applications, as the runway may be re-opened to aircraft quickly, with confidence that the pavement will perform as expected. A brief introduction to the InSiSST ™ facility is provided, however, the primary objective of this paper is to present the results of laboratory and field testing with InSiSST ™ to develop a quality control and assurance test based on fundamental shear properties. To date, these results
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".