A laboratory study on the durability of plant-produced recycled hot mixtures in New Brunswick
Bibliographic record
Abstract
The province of New Brunswick actively uses reclaimed asphalt pavement (RAP) in asphalt mixtures for its highway rehabilitation program. However, limited testing has been conducted to understand these mixtures’ susceptibility to cracking and moisture damage. Stripping is one of the most common pavement distresses in Atlantic Canada due to that region’s climate and geological conditions. This study aims to determine if using RAP in plant-produced asphalt mixes would reduce its durability compared to conventional (i.e., without RAP) mixes manufactured in the same plant. Mixes from three asphalt plants were collected in the field, reheated, and compacted in the laboratory for further testing. The indirect tensile strength test (ITS) was conducted on unconditioned specimens, after a single freeze–thaw and after 50 rapid freeze–thaw cycles. The Illinois flexibility index test (I-FIT) was also performed on all the samples. Four samples were selected for a second round of tests, including Hamburg wheel-track test (HWTT) and ideal tensile asphalt cracking test (IDEAL CT). The ITS and HWTT results show that using RAP did not increase the moisture-induced damage susceptibility; however, RAP mixtures could retain more strength after freezing and thawing. The I-FIT and IDEAL CT tests demonstrate that using RAP could reduce the cracking resistance of the mixes, but the mixes still meet the expected thresholds. The results of this study evidenced that the durability of the mixes was not compromised by the use of RAP and underscored the importance of proper design and adequate binder content.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".