A new pavement design procedure for frost protection in seasonal frost areas
Bibliographic record
Abstract
Differential frost heave and uneven degradation of flexible pavements surface profile in cold climate significantly affects the serviceability of these important civil engineering structures. Among the factors influencing this phenomenon, subgrade soils variability is documented as a significant parameter to consider, especially regarding differential frost heave. Subgrade soils variability may increase in urban area because of the buried utilities and the numerous cuts, both usually filled with granular materials. Therefore, past researches were revisited to implement a design methodology for frost heave protection of flexible pavements in northern environment based on the risk of differential frost heave caused by subgrade soils variability. Previous data were used to 1. Propose a relationship between the maximum pavement IRI, usually encountered at the end of the winter period, the design period and the subgrade soils longitudinal variability index, and 2. Quantify the effect of the replacement of in situ subgrade soils with granular fill on the coefficient of variation of frost heave. The relationship was used as a reference to identify allowable average frost heave based on subgrade soils variability conditions. This is specifically applicable in the urban context for which granular fills, and consequently supplementary induced soil variability, is the main factor contributing to the determination of the allowable frost heave. Working examples of various classes of urban pavements allowed obtaining allowable frost heave criteria that take into account subgrade soils variability, design period and maximum allowable roughness, which are also in good agreement with available data in the literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".