Thermal Aspect of Frost-Thaw Pavement Dimensioning: In Situ Measurement and Numerical Modeling
Bibliographic record
Abstract
The thermal behavior of pavements in winter has a major influence on their dimensioning. The Paris-based Laboratoire Central des Ponts et Chaussées and the Ministère des Transports du Quebec have models to forecast the propagation of frost, frost heave, and thaw events. They have developed a collaborative project to validate these models on an experimental pavement. This pavement was constructed in Quebec in 1998, and its thermal behavior was monitored for 3 years. Two pavements, one with a cement-treated base and one with a hot-mix asphalt pavement, were selected. Two test beds were constructed on each pavement. One test bed was thermally insulated by a layer of extruded polystyrene, and the second was not. The SSR, GEL1D, and CESAR–GELS thermal models; the site and the temperature conditions of the three winters; the pavement structures and their physical properties; the instrumentation setup; and the analysis and comparison of the results of the models among themselves and in relation to the observations conducted on the pavements were assessed. The models provided a satisfactory estimate of frost penetrations, with less than 10% deviation observed between the measured and calculated depths. The deviations between the results of the different models are explained by the differences between the modeling principles. The size and quality of the database constituted will make it possible to improve the thermal forecasting models through more in-depth studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".