Effect of Fines on the Structural Capacity of Urban Base Course Materials
Bibliographic record
Abstract
In flexible pavement design, the granular base course material is designed to dissipate loadings throughout its layer. The design of the granular base is based on an assumed constant structural value over time. However, due to changing moisture regimes and climatic effects, the fines content of the granular base increase over its lifetime. While it is understood that the increased fines content will reduce the structural capacity of the roadway, quantification of its effect on the structural capacity has not been directly measured to better predict the life cycle of the roadway. The objective of this paper was to use mechanistic-climatic materials characterization to quantify the decrease in structural capacity of a City of Saskatoon specified granular base across a range of fines content. The granular base materials studied were typical base aggregate used in the City of Saskatoon and typical low plastic native to the Saskatoon area. Overall, the addition of fines decreased the structural capacity and increased the moisture susceptibility of the granular base. In addition, increased moisture content in the granular base resulted in reduced mechanistic material behaviour. Therefore, with the compounding effects of moisture and increased subgrade fines content in the granular structure can have significant detrimental effects in terms of the life cycle of an urban granular structure particularly in one that is experiencing subsurface moisture issues.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".