Observed evidence of subgrade moisture influence on pavement longitudinal profile
Bibliographic record
Abstract
The relationship between subgrade moisture parameters and pavement profile was explored for 34 asphalt pavements of the seasonal monitoring program (SMP) in the long-term pavement performance (LTPP) program. Volumetric moisture content was quantified in terms of the moisture index (MI), representing moisture as reasonably related to subgrade performance. Using power spectral density (PSD), roughness was evaluated in 21 individual wavebands. From statistical analysis, it was concluded that subgrade moisture significantly affects roughness in the wavebands of 14.9 to 31.2 m and 24.0 to 31.2 m for nonfreezing sites, and in the waveband of 14.9 to 24.0 m for freezing sites. At nonfreezing sites, as the magnitude and variation of moisture increase, pavement surface profile deteriorates quickly. At freezing sites, it was found that moisture variation by freezing contributes to roughness deterioration. Moreover, it was found that pavement thickness and the percent passing the top 0.002 mm of subgrade are significant factors accelerating roughness progression at nonfreezing and freezing sites, respectively.
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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".