Establishment of Network Trigger Values for Pavement Management Rehabilitation
Bibliographic record
Abstract
Most agencies use performance models to forecast future rehabilitation needs using a composite index (Pavement Condition Index- PCI) to provide a single number describing pavement condition. For long term planning when the PCI drops below a certain trigger value rehabilitation is planned. In the 1980’s, Public Works and Government Services Canada, Parks Canada and the Yukon Government introduced the same management systems for their pavements and bituminous surface treatments (BST). One of the challenges was ensuring that the weighting factors for the various distresses correctly reflected pavement condition when the composite indices were calculated. Initially, to aid in this process the rating panel of senior engineers was asked to recommend a rehabilitation strategy based on the field observations that were then compared to the ratings of the various distresses. The objective of this paper is to validate the relationship between trigger values, panel recommendations and composite condition indices. This study involves the comparison of field recommendations and the composite indices for over 3,900 pavement ratings and 7,400 BSTs. It indicates that there is a distinct value of PCI/BCI where the panel recommended a rehabilitation such as an overlay and a further intervention level at which a more major rehabilitation is required.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".