Development of Simple Shear Strength Test for Evaluating Tack Coat
Bibliographic record
Abstract
In general, the stress distribution in asphalt overlays is significantly influenced by the adhesion conditions at the interface between the asphalt overlay and the existing old pavement. To increase bonding between layers, some highway agencies apply tack coats prior to overlay. The opinions among pavement engineers differ regarding the effectiveness of different tack coat materials in enhancing the adhesion between two asphalt layers. This creates a need for developing a simple laboratory test method for evaluating different tack coat materials in terms of their potential to provide adhesion between two asphalt layers. The Ontario Ministry of Transportation initiated a research project in 2003 with the Centre for Pavement and Transportation Technology (CPATT) at the University of Waterloo to develop a test method for evaluating the effectiveness of different tack coat materials used for improving the bonding between two asphalt layers. The main objective of this research was to design a simple and efficient test protocol for the evaluation of tack coats based on adhesion strength, which could be used for evaluating the effectiveness of tack coat in enhancing the bonding between two asphalt layers. This paper describes testing procedures developed using the Marshall loading frame and presents the results of the tests carried out on specimens, with and without tack coat applied at the interface, to evaluate the sensitivity of the proposed simple shear test. For the covering abstrac of the conference see ITRD number E215163.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".