Improving Ontario Pavement Management Through Long Term Monitoring
Bibliographic record
Abstract
This paper presents performance models that have been developed for the Ministry of Transportation of Ontario (MTO) using data from their Pavement Management System (PMS2). This study is in partnership with the Centre for Pavement and Transportation Technology (CPATT), at the University of Waterloo, and the MTO under the Highway Infrastructure Innovation Funding Program (HIIFP). This research includes analysis of historical data from the MTO PMS2. The project involved analyzing 870 sections and over 17,000 pavement treatment cycles for a 20 year cycle. The research involved development of a robust framework for sorting the extensive data and grouping them into categories that reflect typical pavement factors. Performance models were then calibrated, and validated. In the analysis of the historical data, the data was sorted, classified according to pavement type, equivalent total thickness, traffic volume, soil type, and climate zone. In the development of the performance curves 75% of the data was used to calibrate the performance curves, which is described by the predicted pavement condition index (PCI) and as a function of pavement age. The remaining 25% of the data was used to validate the various performance models using various statistical tools. The analysis determines what factors have the greatest influence over performance of the various pavement treatment types. This paper provides a framework for analysis using several statistical tools. It also involves development of expected service lives for various typical pavement treatments under a series of varying conditions in Ontario. This research is important for MTO for validation of existing performance and incorporation for future PMS strategies.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".