Resilient modulus properties of granular highway materials
Bibliographic record
Abstract
An essential component of the new Guide for mechanistic–empirical design of new and rehabilitated pavement structures for the design of flexible pavement structures is the use of resilient modulus for base / subbase materials and subgrade soils. This study reports on resilient modulus (M r ) test results for unbound pavement materials that were obtained according to the American Association of State Highway and Transportation Officials (AASHTO) standard T307–99. Laboratory tests were performed on 36 representative aggregates from across Ontario and empirical relations between M r and the bulk stress were investigated, as well as the sensitivity of M r to moulding water content and gradation. This paper proposes to replace the nonlinear relation between resilient modulus and bulk stress with a linear relation between the two, taking into account the uncertainties that include the effect of varying water content through stochastic analysis. The effects of deviatoric stress on resilient modulus were found to be negligible for the granular aggregates that were tested. The use of a linear relation was computationally more efficient than the use of a nonlinear law; however, differences in strain predictions were observed. The findings from the finite element simulations were consistent to other studies that compared solutions using various constitutive models.
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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) | 0.001 | 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".