Analysis of Variations of Pavement Subgrade Soil Water Content
Bibliographic record
Abstract
Seasonal Monitoring Program (SMP) data available in the Long Term Pavement Performance (LTPP) database DataPave was analyzed to investigate the variations of volumetric water content. The SMP data includes volumetric water contents from time domain reflectometry (TDR) probes in pavement sections located in the United States and Canada. Water content, or the associated degree of saturation, is used to compute resilient modulus for unsaturated unbound base and subgrade soils in the Mechanistic-Empirical Pavement Design Guide (M-EPDG) that was developed for the Federal Highway Administration. The purpose of this paper is to discuss results of analysis of volumetric water content data from the most recent release of DataPave (Release 19) and the resulting variations in resilient modulus that would occur. Results from analysis of the data indicate that there are variations of volumetric water content that occur over time. For a few of the pavement sections, the variations of volumetric water content were seasonal. For most of the sections, it was not possible to determine consistent trends in the moisture variations on a temporal scale or when comparing the different climate zones, soil types (coarse or fine-grained), pavement types or depth to the water table. The volumetric water content variations typically were greater than 3 percent and less than 9 percent. These findings indicate that subgrade soils undergo varying degrees of saturation. The resilient modulus, computed using the water content variations and an empirical equation developed for the M-EPDG, can vary by as much as a factor of 2. The resilient modulus variations are generally higher in wet climates than in dry climates.
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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 | 0.002 |
| 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 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".