Laboratory Investigation of the Performance Evaluation of Fiber-Modified Asphalt Mixes in Cold Regions
Bibliographic record
Abstract
Thermal cracking of pavement is caused by contraction of the asphalt layer at low temperatures, when tensile stresses build up to a critical point at which a crack is formed. The cracks formed then propagate under traffic loading conditions. Freeze-thaw cycles accelerate crack propagation and deterioration of the asphalt layer, and can also lead to the formation of more severe distresses such as potholes. Fibers have attracted increasing attention in the asphalt industry for use as asphalt concrete modifiers. The addition of fibers to hot mix asphalt (HMA) results in a composite material that has a higher tensile strength, along with the ability to absorb greater energy during the fracture process. The fibers within the material also act as a barrier preventing the formation and propagation of cracks in the asphalt mix. This research evaluates the effectiveness of adding polymer fibers to HMA to increase both its resistance to cracking at intermediate and low temperatures, and its rutting resistance and moisture susceptibility at high temperatures. For this purpose, three different types of polymer fibers: aramids, polyethylene terephthalate (PET), and polyacrylonitrile (PAN), were added to conventional HMA mixes. The resulting samples were compacted, and their mechanical properties were compared with conventional HMA in the laboratory. At the end of the paper, a material cost comparison is provided as a reliable source of information when selecting materials to fulfill minimum industry specifications.
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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 | 0.000 | 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) | 0.001 | 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".