Life cycle assessment of asphalt pavement maintenance and rehabilitation techniques: a study for the City of St. John’s
Bibliographic record
Abstract
Although pavement maintenance and rehabilitation (M&R) techniques are usually examined in economic terms, there is a growing need to address their environmental footprints. The objective of this study is to assess the environmental impacts of M&R techniques. Life cycle assessment (LCA) can help in the decision-making process of selecting suitable maintenance techniques based on their environmental impacts. This study investigates: patching, rout & sealing, hot in-place recycling, and cold in-place recycling. Global warming potential (GWP), acidification potential, human health particulate, eutrophication potential, ozone depletion potential, and smog potential are estimated as environmental impacts for each maintenance activity. Materials, equipment use (for construction and M&R), and transportation were the main elements considered. A sensitivity test is performed to identify the significant factors for the LCA. The study concluded that GWP was the most important impact category. Rout & sealing and cold in-place recycling produced the lowest GWP emissions. Notably, pavement patching and hot in-place recycling showed significant detrimental environmental impacts.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".