Optimization of Joint and Crack Sealant Selection Criteria Based on Laboratory and Field Performance
Bibliographic record
Abstract
The optimized selection of joint sealants can extend pavement service life and reduce annual maintenance and rehabilitation needs particularly in regions which experience extreme climatic conditions. Early sealant materials were not subjected to standardized testing procedures and many failed as a result. Since then, several empirical test procedures have been proposed and a few have been adopted into approved standards, by bodies such as the American Society for Testing and Materials (ASTM). Variability within the sealants, their application methods, and the empirical nature of the test methods made it difficult to predict sealant behaviour in the field. The purpose of this research was to develop a performance-based laboratory testing approach, and to investigate and rank the performance of eight types of hot-pour joint and crack sealants for applicability of use in Manitoba. The project involved laboratory testing of sealants to verify fundamental properties and performance simulation under cyclic loading at three test temperatures. In an effort to optimize the sealant selection criteria, the laboratory performance is compared with field performance in a controlled field trial. The trial involved evaluation of the failure rates of sealants on an asphalt pavement section on the TransCanada highway, which is the primary highway connecting Canadian provinces.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".