Environmental benefits of warm mix asphalt technologies: Experience of the city of Calgary
Bibliographic record
Abstract
Development of Sustainable pavement infrastructure development with environmentally friendly alternatives is preferred to reduce greenhouse gas emissions.Warm mix asphalt (WMA) is identified as one of the alternative to the typical hot mix asphalt (HMA) used on pavement construction to reduce these emissions.WMA technology allows an asphalt mix to be prepared and placed at lower temperatures than conventional hot mix.This study is focused on a comparative study of three WMA mixes and a HMA control mix used in the construction of an environmentally focused subdivision in the City of Calgary, Alberta, Canada.The scope of the study covers aspects related to asphalt plant production, asphalt plant emissions, construction, laboratory performance, and initial road performance with different WMA technologies in comparison to HMA.Paper reports on the findings related to the asphalt plant production, asphalt plant emissions, and construction stage of the study.The advantages of using WMA technology were evidenced during the mix manufacturing and road construction stages.The WMA mixes showed reduced emissions, lower fuel consumption, reduced smoke and odors, improved safety and working environment, improved mix workability, extended compaction time, more uniform compaction, and reduced thermal segregation.The laboratory evaluation showed that the WMA mixes behaved similar or superior to the HMA mixes.WMA mixes had similar rutting and fatigue resistance, better low temperature behavior, higher laboratory workability, and similar stripping susceptibility, than the conventional HMA mixes.However WMA mixes showed slightly lower mix stiffness at high temperature compared to HMA mixes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".