On the resilient behaviour of unbound aggregates
Bibliographic record
Abstract
This paper explores the potential of the resilient modulus test to characterize the mechanical response of unbound aggregates. The paper is divided into two sections. The first section presents the results of a laboratory study aimed at identifying the significant factors that affect the resilient modulus of granular materials. Using the principles of experimental factorial design, four parameters, namely: deviator stress, confining pressure, moisture content and material dry density were included in the investigation. Resilient modulus test results showed that the effect of the deviator stress was the most significant followed by the effect of moisture content. Other factors appear to have little or no effect at all on the modulus parameter. In the second section of the paper, an attempt is made to interpret the material micro behaviour at the grain level with its macro response measured by the resilient modulus parameter. This part of the study was carried out using the discrete element modelling technique. Theoretical values of the resilient modulus obtained satisfactorily agree with laboratory determined moduli. These results confirm the suitability of the resilient modulus test to describe the mechanical response of unbound aggregate materials to traffic and environmental stimuli.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".