Development of a New Mechanistic Index to Predict Pavement Performance during Spring Thaw
Bibliographic record
Abstract
In northern climates, frost action is a major cause of pavement deterioration. It is well known that freezing temperatures in frost susceptible subgrade soils cause the pavement to heave. The resulting displacement imposed to the pavement can be substantial, reaching 100 to 200 mm in severe cases. When spring thaw occurs, segregated ice melts generating high pore pressures in frost susceptible subgrades. The bearing capacity loss can be substantial, leading to important structural damage to pavement exposed to severe and uncontrolled loading conditions. Considerable research efforts have been devoted to the characterization of soils and material properties and their variation as a function of moisture and temperature variations. These research initiatives are typically conducted in the field using deflexion measurements or in the laboratory using cyclic triaxial testing. The use of these research results typically requires assessing seasonal fatigue damage using analytical techniques to compute stresses and strains at critical location in pavement structures and empirical damage models. A new approach based on a mechanistic index is proposed. The index is taking into consideration the amount of water accumulated by the freezing process, the rate of thawing in pavement layers and in the subgrade soil and the rate of consolidation of the pavement structure. The paper will describe the theoretical development of the new index and will present correlation between the index and the characteristics of observed weakening (intensity and duration) during spring thaw.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".