Integrated approaches for predicting soil-water characteristic curve and resilient modulus of compacted fine-grained subgrade soils
Bibliographic record
Abstract
This paper combines a series of approaches for predicting the soil-water characteristic curve (SWCC) and the variation of the resilient modulus (M R ) of compacted fine-grained subgrade soils with moisture content, which is the key information required in mechanistic pavement design methods. The presented approaches for the SWCC and M R are integrated, as (i) they are developed following the same philosophy, (ii) they require only the measurements of the suction and moisture content or M R at saturated and optimum moisture content conditions for prediction, and (iii) the predicted SWCC is used for predicting the M R – moisture content relationship. Experimental studies have been performed on five fine-grained subgrade soils that were collected from different regions in Ontario, Canada, to determine their M R at various external stress levels and post-compaction moisture contents, as well as their SWCCs after the M R tests. Experimental measurements are predicted using the integrated approaches and the empirical approaches currently used in the mechanistic–empirical pavement design guide (MEPDG). It is demonstrated that the integrated approaches are easy to use and show improved reliability in predicting both the SWCC and M R for the investigated subgrade soils in spite of using limited experimental data.
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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".