Life cycle analysis for asphalt pavement in Canadian context: modelling and application
Bibliographic record
Abstract
Pavement Life Cycle Assessment (LCA) is a comprehensive method to evaluate the environmental impacts of a pavement section. It employs a cradle-to-grave approach assessing critical stages of the pavement’s life. Previous LCA case studies used a wide variety of different functional units and factors in order to achieve different goals and scopes. These inconsistent functional units and factors create confusion in understanding the complete picture of environmental impact during the initial construction. Therefore, a set of models of pavement LCA considering every factor of the pavement life cycle phases is needed. Canada is a very large country and the different provinces have different pavement construction practices. Therefore, the goal of this work is to develop a set of useful models for quantifying CO2 emission from pavement construction in Canada. A total of 141 Canadian road sections from the Long Term Pavement Performance (LTPP) database are considered to develop models using machine learning algorithms: multiple linear regression, polynomial regression, decision tree regression and support vector regression. These models determine the significant contributors and quantify the CO2 emission in material production, initial construction, maintenance and use phase. The study also reveals the contribution of Canadian provinces’ CO2 emission involved in the life of a pavement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".