Implementation of Cold In-Place Recycling with Expanded Asphalt Technology in Canada
Bibliographic record
Abstract
Cold in-place recycling (CIR) is a pavement rehabilitation method that processes an existing hot-mix pavement, sizes it, mixes in additional asphalt cement, and lays it back down without off-site hauling and processing. The added asphalt cement is typically emulsified asphalt. A recent development in CIR technology is the use of expanded (foamed) asphalt rather than emulsified asphalt to bind the mix. This combination of CIR and expanded asphalt technologies is termed cold in-place recycled expanded asphalt mix (CIREAM). The Ministry of Transportation Ontario (MTO) constructed a CIREAM trial section on Highway 7 in July 2003. The 5-km CIREAM trial section was constructed adjacent to an 8-km section on which conventional CIR was performed. CIREAM placement resulted in a smooth, hard, uniform surface that provided an excellent platform for paving operations. The CIREAM placement progressed in a continuous and efficient manner, with 5 km placed over a 3-day period. Indirect tensile strength testing was carried out on both materials during construction. Falling weight deflectometer (FWD) testing and evaluation of pavement roughness and rutting by the use of MTO's automatic road analyzer (ARAN) were carried out. Resilient modulus testing of core samples of the CIR material and CIREAM was also carried out. The results of the FWD, ARAN, and resilient modulus tests indicated that the CIR and CIREAM pavements were performing similarly. A field review 1 year after construction showed no discernible distortion, rutting, or cracking. On the basis of short-term results, CIREAM appears to be an acceptable in-place recycling and rehabilitation strategy that provides an economical alternative to conventional CIR, reduces curing time, and extends the construction season.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".