Development of a Test Method to Evaluate Durability of Pavement Joints under Winter Conditions
Bibliographic record
Abstract
In areas with cold winters, premature failure in concrete pavement joints is a frequent phenomenon, yet there is no standard test method for evaluating their durability. In this study, the effectiveness of several evaluation techniques for concrete joint durability were evaluated. Concrete slabs with saw cuts representing joints were made, and exposed to different freezing and thawing/ wetting cycles, and different salt solutions (NaCl, CaCl2, and MgCl2). The durability of the joints was evaluated using measures of strength loss, mass loss, and a visual inspection. It was found that under these exposure conditions, the strength loss and visual inspection modes of evaluation could discern different levels of damage between different types of exposures, and also different concrete mixes (e.g. Different cement contents, strength and the presence or absence of SCM). Further research is required to evaluate the effects of sealants, saturated bases, and higher salt concentrations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".