Time-domain reflectometry measurements and soil-water characteristic curves of coarse granular materials used in road pavements
Bibliographic record
Abstract
Coarse granular materials are used extensively in road construction. Bearing capacity can be affected by the water content in the layers of these materials. The ability to estimate water content and to infer water movements is therefore important. The purpose of the work described herein was to determine soil-water characteristic curves and the relationship between relative apparent permittivity and volumetric water content for coarse (maximum particle size 90 mm) granular materials having various gradations. The relative apparent permittivity was measured with the aid of time-domain reflectometry (TDR), and the concurrent matric suction was measured with a tensiometer. Samples were prepared in a steel box and were heavily compacted, and TDR probes and a tensiometer cup were buried within the matrix. The variation in volumetric water content with apparent relative permittivity was found to deviate from the Topp et al. relationship. Soil-water characteristic curves were described using the Brooks–Corey and van Genuchten models. A pronounced hysteresis between wetting and drying paths was observed. For the low water retention coarse materials, measurements of water content might, in general, require correction because of the nonlinear distribution of water in the sample.Key words: pavement, time-domain reflectometry, soil-water characteristic curve, granular material.
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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 | 0.001 |
| 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) | 0.000 | 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".