Seasonal and diurnal variations of back-calculated layer moduli in flexible pavement and FWD testing guidelines
Bibliographic record
Abstract
Seasonal and diurnal variations can significantly impact falling weight deflectometer (FWD) measurements and, thus, deflection-based parameters such as back-calculated layer moduli. Therefore, it is necessary to evaluate and quantify these effects. The back-calculated layer moduli data from the Long-Term Pavement Performance Seasonal Monitoring Program study were analyzed within each climatic region. The effects of seasonal and diurnal changes were quantified and related to ambient temperatures were related to the development of general measurement guidelines. The HMA layer moduli showed minimal variation during the spring and fall seasons in every climatic region and were consistently higher based on the deflections measured before noon. However, temperature correction, generally applied to the HMA layer moduli, can eliminate the temperature impacts of the FWD measurements. Because the unbound layer moduli are back-calculated from the single measured deflection basins on the surface, temperature and moisture conditions at the time of measurements can affect the material properties. The base and subgrade moduli showed minimal variation within the temperature ranges of the spring and fall seasons. Therefore, such temperature within each climatic region may result in layer moduli values closest to their representative in-field conditions. In addition, the unbound layer showed no effect of time within a day, suggesting that FWD testing can be conducted at any time during the day. Based on the results, the preferred temperature ranges for FWD measurements are 55–70 °F and 65–75 °F in the freeze and non-freeze regions, respectively. FWD measurements can be conducted at any time during the day. The preferred seasons for testing are spring and fall in all climatic regions.
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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) | 0.000 | 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".