Characterizing the Soil Resilient Modulus for Typical Manitoba Soils
Bibliographic record
Abstract
Resilient modulus of unbound materials is a fundamental property that is required for pavement design and estimation of its remaining service life. This paper highlights efforts to quantify the resilient modulus of subgrade soils in Manitoba. The research has two main objectives. The first objective is to model the relationship between the resilient modulus and cyclic stress, confining pressure, moisture content and dry density for typical Manitoba subgrade soils. The second objective is to evaluate the effect of basic soil improvement techniques. The resilient modulus test is performed on three types of soils: silty sand (from central & southern Manitoba), sandy clay (from western Manitoba), and high plastic clay (from Red River Valley). Soil samples are prepared at four moisture contents and dry densities. The moisture contents were selected such that two moisture contents are on the dry side (below the optimum moisture content) and the other two are on the wet side (above the optimum moisture content), according to the Standard Proctor Compaction Curve. Each sample is subjected to sixteen loading combinations that constitute a range of cyclic loads and confining pressures. The values of resilient modulus obtained from these tests will be incorporated in the structural design of new pavements. These values will also be used as base values to evaluate the adequacy of basic soil improvement techniques.
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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 | 0.000 |
| 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) | 0.001 | 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".