Mechanical and drainage performance characterization of unbound granular materials
Bibliographic record
Abstract
The drainage performance of unbound granular material (UGM) is an important consideration in pavement design because the presence of excess moisture in UGM layers can eventually lead to premature failures. Recently, transportation agencies have been evaluating their granular base and subbase drainage and mechanical performance to ensure sufficient drainage capacity while maintaining adequate structural support to produce more sustainable pavement structures. Linking performance to UGM construction specification requires accurate characterization of UGM’s mechanical and drainage performance and how physical and gradation parameters affect such performance. These evaluations led to an update of the specification requirements of UGM in many jurisdictions including Manitoba. In this research, constant head hydraulic conductivity, resilient modulus, permanent deformation, double ring infiltrometer, and falling weight deflectometer test methods were used in laboratory and field investigations. These tests were conducted to characterize the drainage and mechanical performance of ten UGM samples representing four different gradation bands. The laboratory test results were also used to investigate the reliability of the estimated hydraulic conductivity from the Moulton prediction model and from the Enhanced Integrated Climatic Model (EICM). Test results showed an improvement in resilient modulus and drainage quality for samples in gradation bands that specify larger maximum aggregate size and limited fines. A statistical analysis of the test results showed that D10 larger than 0.2mm and D60 larger than 8mm would guarantee higher stiffness and better drainage performance with a time-to drain of less than 5days for typical pavement cross-sections and a resilient modulus value exceeding 200MPa. The Moulton prediction model was found to provide a better approximation of hydraulic conductivity of the materials included in this study, while the EICM model was found to significantly overestimate the hydraulic conductivity for most of the 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) | 0.000 | 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".