Dedicated Pavement Type Networks Based On a Probabilistic Life-Cycle Cost Analysis
Bibliographic record
Abstract
In 2001, the Québec Ministry of Transportation (MTQ), in Canada, adopted a policy subdividing the existing pavement network into dedicated concrete and asphalt networks. This paper summarizes the methodology used for determining these respective networks using probabilistic Life Cycle Cost Analysis (LCCA) and provides an assessment of the situation four years after implementation. Sixteen (16) different combinations of Average Annual Daily Traffic (AADT), number of lanes, truck percentage, and truck factor yielded 32 pavement designs in asphalt and concrete that were compared in pairs over an analysis period of 50 years using a probabilistic LCCA software. Regression equations were used to generalize the results of these standard cases. The network allocation criteria were determined by applying dominance tests to these probabilistic results; in the areas without clear dominance further analyses would be necessary using project-specific data. After minor adjustments to ensure continuity, the respective networks (“white” for concrete, “black” for asphalt and “gray” for further analysis) were established officially. Since 2001, the clarity and convenience of this policy were seemingly appreciated both by the industry and MTQ officials
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".