Assessment of thaw weakening in pavement stiffness using the spectral analysis of surface waves
Bibliographic record
Abstract
Mechanistic methods for the design of pavement structure in cold regions require an adequate knowledge of the seasonal variations in elastic properties of the structural layers. In this study, the spectral analysis of surface waves (SASW) method was adopted to monitor the changes of the stiffness modulus in pavement during a complete freezethaw cycle. The SASW tests were performed on a section of pavement in Québec City over a complete freezethaw cycle in 2001. The stiffness profiles of the pavement layers were back-calculated from the experimental dispersion curves using a forward-modelling approach based on a discrete stiffness matrix method. The seasonal variations in stiffness in the base, subbase, and subgrade were then assessed. The thaw-weakening period and the recovery in stiffness after the complete thaw were observed. The minimum value of the stiffness modulus in the base layer was about 80% of its prefreezing value, and those of the subbase and subgrade were about 60%. A sharp change in the back-calculated moduli at temperatures close to the freezing point was observed. The relevance of using the SASW method to study the thaw weakening and recovery in pavement stiffness affected by freezethaw cycles is clearly shown in this study.Key words: SASW, pavement, seasonal variation, stiffness, thawing, recovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".