A Comprehensive system for characterizing granular materials: providing material input for pavement design
Bibliographic record
Abstract
The new 2002 AASHTO Guide for Pavement Design advocates the use of the resilient modulus parameter for the characterization of granular materials used as base and sub-base layers in flexible pavements. The multitude of factors that affects the resilient behaviour of granular material makes the determination of the resilient modulus at different loading and physical conditions a critical factor for the pavement design process. This calibration task requires the availability of a robust system capable of producing the needed results in a timely and effective fashion. The current paper reports on recent research activities at the National Research Council, which aim at providing tools that can be utilised to carry out the calibration process. The objective of the research program is to establish database of resilient moduli for the different types of granular materials used in the Canadian Provinces. The research program combines both laboratory testing and numerical techniques to develop the required database. The paper presents an overview of the characterization system together with typical results obtained. Application of the new system to quantify the effect of compaction density, a major construction factor affecting the behaviour of granular material, on the resilient modulus is illustrated. Preliminary results obtained from repeated load tests and discrete element modeling (DEM) confirm the adequacy of the developed tools to produce the sought database.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".