Assessing and Using the Segregation Potential in Pavement Engineering
Bibliographic record
Abstract
During winter, frost penetrates in pavement materials and subgrade soils. When the frost front reaches frost susceptible subgrade soils, water is sucked toward the frozen fringe where ice lenses are formed. Heave of the pavement surface resulting from these phenomena can reach and even exceed 150 mm for climatic conditions prevailing in Canada. Frost heave in soils is the result of the combined action of heat and moisture transfer in freezing soils. Freezing soils in a pavement system are subjected to a thermal gradient. As a result, a negative pore water pressure gradient is created behind the freezing front, in a thin layer of partly-frozen soil known as the frozen fringe. Based on this understanding of the frozen fringe conditions, Konrad and Morgernstern (1980) have developed the segregation potential concept to model one dimensional frost heave in soils. The segregation potential is a mechanistic index allowing for the quantification of the frost susceptibility of a given soil in specific climatic conditions. The segregation potential concept has been used for more than two decades for several engineering applications. The paper will describe how the segregation potential can be assessed using a freezing test or estimated using various approaches. It will also describe how the segregation potential can be used to perform frost analysis of pavements subjected to cold climates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".