Determining Subgrade Resilient Moduli for Pavement Designs
Bibliographic record
Abstract
This case study reports on the use of a high-capacity falling weight deflectometer and dual mass dynamic cone penetrometer, as well as a parallel laboratory testing program, to determine representative subgrade moduli for a major design-build urban expressway in Edmonton. It was found that the resilient moduli of samples prepared at optimum water content, or less, according to AASHTO T307-99 were extremely high, deviating from the typical Mr ~ 10 CBR (MPa) by more than a factor of 2. Additional tests completed on a sample soaked for two days before testing and samples prepared at water contents consistent with conditions corresponding to the soaked CBR revealed that two days soaking was insufficient for the fine-grained soil to soften enough to cause a substantial loss in material stiffness, with the resilient modulus of samples prepared at water contents corresponding to the soaked CBR relating well to the relation Mr ~ 10 CBR. Good agreement was also found to exist with results obtained in the laboratory and those estimated from the in-situ tests. Considerable care must be taken with respect to selecting representative field moisture contents for preparing laboratory Mr-samples.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".