Evaluation of Seasonal Variation in Mechanistic Responses of Flexible Pavements through use of Falling Weight Deflectometer Data
Bibliographic record
Abstract
Flexible pavements in cold regions with frequent freeze and thaw cycles are prone to damage at the start of thawing and during the thaw–recovery season every year. In an attempt to limit damage to the pavement, many highway agencies enforce a spring road ban (SRB) on secondary roads during the thawing period. The Alberta Ministry of Transportation in Canada performs an extensive falling weight deflectometer (FWD) test program during the spring to enhance SRB decision making. This study uses FWD test data from 2000 to 2006 for multiple highway sections in Alberta to investigate the seasonal variation in pavement stiffness and its effect on pavement mechanistic responses. FWD data were used to backcalculate the moduli for the pavement layers, which were then used in multilayer elastic models to predict the pavement critical responses in thawing versus recovery periods. Asphalt Institute models were used to relate the pavement critical responses to fatigue cracking life (N f ) and rutting life (N d ) in thawing versus recovery periods. N f and N d dropped by as much as 80% for fatigue cracking and 95% for rutting in the thawing period compared with the recovery period. Further analysis showed that a 50% reduction in the load applied during thawing can result in an approximately 90% increase in both N d and N f . The Mechanistic– Empirical Pavement Design Guide software was used to simulate SRB by reducing the maximum applied load by 50% in the thawing period. The results showed a 50% increase in pavement life in rutting and in top-down and bottom-up fatigue cracking.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".