Laboratory Investigations of Cold Mix Asphalt for Cold Region Applications
Bibliographic record
Abstract
Abstract Cold mix asphalt (CMA) can be a quick, environmentally friendly, and low-cost option for utility-cut backfilling on urban streets and most highway agencies prefer it as a pothole patching and pavement surface repairing material over hot mix asphalt (HMA) during winter and wet seasons. However, applying poor quality CMA may result in premature patching and backfilling failures, reduce pavement’s integrity and longevity, and impair drivers’ safety. Additionally, CMA’s lack of stability and durability while exposed to heavy traffic, moisture, and freeze-thaw conditions may accelerate further deterioration. This paper focused on evaluating and comparing twelve CMAs through laboratory tests to determine properties that may cause poor performance in cold climatic regions. Taking into consideration the identified CMA distresses, this study conducted Marshall Stability and flow, indirect tensile strength (ITS), cohesion, and adhesiveness tests on nine proprietary and three conventional cold mixes, including both open- and dense-graded materials. Most mixes had low adhesion properties and high sensitivity to freeze-thaw cycles. An analysis of variance showed that aggregate grain size distribution and bitumen content had a significant effect on CMA’s performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".