Seasonal Load Restrictions on Low Volume Highways: Pavement Strength Estimation
Bibliographic record
Abstract
In Ontario, low volume roads comprise about 20% (3,715 center line kilometers) of the total provincial highway network. These roads are subjected to infrequent but intensive traffic loading as well as a high number of freeze-thaw cycles. Seasonal load restrictions (SLRs) are applied during the spring thaw to low volume highways which are not structurally designed to carry heavy loads during saturated periods. Currently, methods used to apply SLRs are based on visual observation, field testing, prescheduled dates, and empirical models. There is a need to develop a rational, quantitative procedure to determine the best time to apply SLRs based on measured or predicted frost conditions and pavement response. A primary objective of this research is to develop models that can be used to estimate the pavement strength as a function of frost/thaw depths, characteristics of pavement structures and other variables. For this purpose, the first step is to explore the application of thermal numerical modelling to estimate frost/thaw depths based on variables related to climate, pavement, base, sub-base and sub-grade conditions. Once these models are developed and calibrated, the second step would be to relate the frost/thaw depths to pavement strength. The work of this research is in progress and this paper presents preliminary results obtained up to date.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".