Influence of Accelerated Curing on Cold In-Place Recycling
Bibliographic record
Abstract
This work is a continuation of the SCORE research program, which was dedicated to cold recycling using asphalt emulsions. The goal here was to investigate curing time and conditions at elevated temperatures and lower humidity in order to predict material behaviour after several years in service. A comparative test program was conducted among Eurovia Services and Czech Technical University laboratories with the emphasis of curing practices that would lead to the improvement of mechanical properties. Different curing times (from 1 hour to 28 days), at different temperature (18 and 60 deg C) with different moisture contents were also tested at the Ecole de technologie superieure. The European samples were compacted using 5 MPa of static compression and they were evaluated using the indirect tension and stiffness modulus testing. The Canadian samples were compacted with a Marshall hammer and tested in Marshall stability. Samples were also tested in rutting resistance and in thermal cracking resistance. Conclusions of this study were compared with the literature and similar research. An increase in moisture content results in a decrease of stability and modulus. An accelerated cure of 24 hours at 60 deg C seems to give good stability. The CIR materials show good rutting resistance and good thermal cracking resistance. (A) For the covering abstract of this conference see ITRD number E220283.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".