Optimal Programming of Pavement Maintenance and Rehabilitation Activities for Large-Scale Networks
Bibliographic record
Abstract
Under ever-increasing budget restrictions, the optimal programming of maintenance, rehabilitation, and reconstruction of pavement networks becomes crucial. This paper provides and tests two computationally effective approaches that, in accordance with proposed problem-reduction techniques, may be used for multi-year programming of large-scale pavement networks. The two approaches are the Holistic approach, which tackles the entire planning period at once, and the Sequential approach, which breaks down the planning period and optimizes the work plan for each year separately. The developed approaches were applied to the 2013 dataset of the Quebec highway network, which consists of approximately 16,000 roadbed centerline miles. The application of the methods to the case study shows that both Sequential and Holistic approaches can be used effectively for planning large-scale networks with a reasonable analysis run time. This study provides insights into the behavior of large-scale networks and helps transportation agencies make more consistent and effective decisions regarding the allocation of limited funds by using the proposed methodology.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".