Use of falling weight deflectometer time history data for the analysis of seasonal variation in pavement response
Bibliographic record
Abstract
This paper presents the results of an exploratory analysis of falling weight deflectometer (FWD) data collected on a large project about the spring thaw behaviour of pavements. The test site includes four test sections, two of which are conventional flexible pavement structures, whereas the other two are built with a cement-treated base. The aim of this study is to verify the applicability of using FWD time history data to evaluate damage to a road during the thawing period. The applicability of the analysis techniques is verified through the phase angle and dissipated energy. The data analyzed were obtained from tests conducted with an FWD on one flexible pavement test section. The results obtained showed a clear difference between the winter, thawing, and summer periods. It was found that the phase angle and dissipated energy can be used to evaluate the road damage during the thawing period through quantification of the phase angle and dissipated energy. These factors can also be used to describe the pavement behaviour in terms of elasticity and viscoelasticity.
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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 | 0.002 | 0.000 |
| 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) | 0.000 | 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".