Development of Preventive Maintenance Decision Trees Based on Cost-Effectiveness Analysis: An Ontario Case Study
Bibliographic record
Abstract
Various transportation agencies have begun to consider implementing preventive maintenance (PM) strategies as part of their regular pavement management programs. To determine whether a PM strategy is more cost-effective than a conventional maintenance strategy, various technical and economic analyses were carried out. Currently, most agencies have limited information on the cost-effectiveness (CE) and long-term performance of PM strategies, so it is difficult to determine when and where these treatments should be used. The use of PM treatments based on a CE calculation and analysis is examined, and a decision tree (including treatments and strategies) is developed for each functional pavement class of the Ontario road network. Pavement data from the Ministry of Transportation of Ontario are used to perform a CE calculation for each suggested PM treatment and strategy. On the basis of a comparison and analysis of CE calculation results, guidance is provided on the right treatment, time, and strategy cost level for each functional pavement PM program within the Ontario environment. The results are summarized in the form of a decision tree.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".