Comparative Performance of Conventional Cold In-place Recycling to Cold In-place Recycling with Expanded Asphalt: Ontario’s Experience
Bibliographic record
Abstract
The Ministry of Transportation Ontario (MTO) constructed its first 5-km trial section of Cold In-Place Recycled Expanded Asphalt Mix (CIREAM) on Highway 7, east of Perth in July 2003, adjacent to 7-km of Cold in-place recycling (CIR) mix. Conventional cold in-place recycling (CIR) is an established pavement rehabilitation method that processes an existing hot mix asphalt (HMA) pavement, sizes it, mixes in additional emulsified asphalt, and lays it back down without off-site hauling and processing. A recent development in CIR technology termed Cold In-Place Recycled Expanded Asphalt Mix (CIREAM) is the use of expanded (foamed) asphalt, rather than emulsified asphalt to bind the mix. Both technologies are considered to be a sustainable pavement rehabilitation that generates less greenhouse gas emission. Both CIR and CIREAM pavement sections were uniform in appearance and performed similarly under traffic. The sections now have 5 years of performance data and the visual distress survey indicated the pavement is performing very well. Automatic Road Analyzer (ARAN) was carried out on an annual basis to evaluate pavement roughness and rutting. Falling Weight Deflectometer (FWD) testing was also carried out annually to compare the strength of CIR and CIREAM. Results of an ANOVA analysis of the ARAN and FWD data show the two sections are performing statistically the same. Summary of the resilient modulus and indirect tensile strength of the two mixes are presented. The five year performance data for CIREAM indicates that the technology is a promising alternative to conventional CIR. MTO will continue to monitor the long-term performance of this innovative rehabilitation technology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".