Using Road Weather Information Systems (RWIS) to Control Load Restrictions on Gravel and Surface-Treated Highways
Bibliographic record
Abstract
This research is the second phase of a two-year project conducted at the University of Waterloo (UW) in association with the Ministry of Transportation of Ontario (MTO). During the first phase of the project, experimental sites were installed in Northern Ontario to monitor the amount of frost in the pavement and a preliminary analysis of field and RWIS data found a reasonable correlation between frost depth in the roadway and RWIS variables. To fulfill the ultimate objective of this project, which is to assist the MTO in making effective real time Spring Load Restrictions (SLR) and Winter Weight Premiums (WWP) decisions using Road Weather Information Systems (RWIS) real-time data and forecasts, the second phase of the project followed up by developing empirical predictors for the depths of frost and thaw as well. In addition, deflection data was collected using a portable Falling Weight Deflectometer (PFWD) and the measurements were examined. In addition, the Mechanistic-Empirical Pavement Design Guide (MEPDG) software was used respectively to quantify how much pavement distresses were impacted by various loading scenarios and to simulate the ability of each load restriction scenario to increase pavement service life for typical Northern Ontario structure, climate and traffic conditions. The Ontario Pavement Analysis of Costs (OPAC 2000) software was used to perform a life-cycle cost analysis and evaluate the benefit of imposing SLR when the pavement is weak in terms of pavement preservation and service life. Finally, the findings are organized into practical recommendations for the determination of SLR and WWP schedules, and the overall need for collection of Ontario frost and deflection data on low-volume roads with the objective to calibrate the model to various Ontario regions on the one hand and track spring-thaw weakening in the pavement on the other hand is outlined in this final report.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".