Life Cycle Cost Analysis of Municipal Pavements in Southern and Eastern Ontario
Bibliographic record
Abstract
Many municipalities are seeking ways to more efficiently manage budgets and improve roadway performance. While there are many pavement types available to municipalities, the most common alternatives have historically been asphalt and concrete pavements. The recently released mechanisticempirical pavement design guide pavement design procedure and associated software application (DarwinME) has provided pavement designers with a very comprehensive procedure to develop specific pavement designs that will suite the purpose of the agency while minimizing costs. More robust design inputs have led to improvements in the design of both asphalt and concrete pavements based on long term pavement performance. The designs, maintenance and rehabilitation plans developed for this project are able to sustain an adequate level of service for Ontario municipalities over a 50 year service life. Pavement type selection is one of the more challenging engineering decisions facing roadway administrators. The process outlined in the paper includes a variety of engineering factors such as materials and structural performance which must be weighed against the initial and life-cycle costs, as well as, sustainable benefits. The technical part of the evaluation includes an analysis of pavement lifecycle strategies including initial and future costs for construction and maintenance activities. For the covering abstract of this conference see record control number 201111RT334E.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".