Life cycle assessment of lightweight cellular concrete subbase pavements in Canada
Bibliographic record
Abstract
The use of Lightweight Cellular Concrete (LCC) as a subbase alternative to typical flexible pavement subbase material in cold regions is gaining attention. Since a setback of using LCC is its high cement content, which is a significant source of carbon dioxide (CO2) emissions, it becomes imperative to consider this aspect when proposing LCC for pavement applications. This study evaluated the environmental impact of three densities (400, 475, and 600 kg/m³) of LCC produced with two methods (wet and dry mix) and compared to two types of unbound granular material (granular A and B) as flexible pavement subbase. The pavement performance was predicted using MEPDG and WESLEA software and compared with measured field responses as a basis for maintenance strategy. The Life Cycle Assessment (LCA) results showed that lower-density LCC pavements decreased environmental costs by reducing total life CO2 emissions by up to 16% and reducing environmental impact for Carbon monoxide, Sulphur dioxide, Nitrogen oxide, Particulate Matter (PM)2.5, PM10 and total PM compared to granular A and B pavements. As LCC density increased, more emissions occurred, showing the importance of density to emissions output. This research demonstrates the potential benefits of using LCC as an alternative subbase pavement material.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".