An Approach to Applying Spring Thaw Load Restrictions for Low Volume Roads Based on Thermal Numerical Modelling
Bibliographic record
Abstract
Jurisdictions in Canada and the northern United States apply spring load restrictions (SLRs) to low volume roads to mitigate damage to these roads during spring thaw. Ideally, the SLRs should commence when the thaw front has begun to penetrate sub-base materials, and end when the pavement system is completely thawed (Andersland and Ladanyi, 2004). Methods involving visual observations, field testing, prescheduled dates and empirical models are currently being used to apply SLRs. These methods, however, do not readily allow variations in climatic conditions and pavement system properties, such as soil type and water content, to be accounted for in a comprehensive manner. To this end, thermal numerical modeling is being conducted for the purpose of developing guidelines to apply SLRs. Time variations of frost and thaw depths were simulated using the finite element thermal model TEMP/W (by GEO-SLOPE International Ltd.) and are compared with measured frost and thaw depths at an instrumented site in northern Ontario. A particular focus of this work is on the thermal boundary conditions applied at the upper and lower boundaries of the thermal model. Modelling results to date indicate that penetration of the frost front over the winter can be simulated fairly well, with a good match to the measured results. Modelling the thaw front penetration was found to be quite dependent on the upper thermal boundary condition. An approach in which an "n-factor" is used to estimate pavement surface temperatures from measured air temperatures best simulates the measured progression of the thaw front. A floating reference temperature approach, to better account for increasing solar gain during the spring thaw period, is also examined.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".