Variations in Composition and Rheology of Bituminous Crack Sealants for Pavement Maintenance
Bibliographic record
Abstract
Bituminous crack sealants are used for the preventive maintenance of asphalt concrete pavements. The selection of a durable sealant can be difficult, however, mainly because of the lack of correlation between standard sealant specifications and field performance. Hence, an approved list of materials based on past performance is sometimes used to select sealants. However, sealant durability and performance vary over time. To investigate the effect of sealant lot variation on sealant properties, six lots of two sealants from different suppliers were analyzed for filler and polymer contents and rheological response. It was found that the difference in composition and rheology between lots can be similar to that between sealants produced by different manufacturers. Hence, sealant lot-to-lot variation can partly explain the variation in the field performance of sealants. Therefore, lists of approved products drawn from the field performance of past years are ineffective in the selection of sealants for future maintenance. The application of segregated sealants was also investigated, including assessing the effect of melter stirring on sealant homogeneity and measuring the segregation of sealant upon cooling. It was found that sealants do not segregate after their application and subsequent cooling and that a rapid circumferential stirring of 25 revolutions per minute in the heating kettle allowed for the remixing of a segregated sealant.
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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 | 0.002 |
| 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".