Mechanistic Comparison of Cement- and Bituminous-Stabilized Granular Base Systems
Bibliographic record
Abstract
The Saskatchewan, Canada, Department of Highways and Transportation is investigating alternative recycling and strengthening systems for inservice thin granular pavements. This research is being performed to improve the granular pavement structural integrity and to reduce the dependence on new source aggregates. A pilot project investigated the mechanistic-climatic laboratory characterization of materials used to construct test sections on Control Section Highway 15-11 (C.S. 15-11). This research demonstrated the use of ground-penetrating radar and falling weight deflection measurements to select uniform field test section locations. In situ recycled granular base was sampled and found to be a typical thin granular pavement requiring strengthening because it is relatively high in fine sand fraction and has a high portion of intermediate plastic clay fines. These two properties are known to cause marginal performance of granular bases in the field. This research showed that cement and bitu-minous stabilization significantly improved the mechanistic primary response and climatic durability properties of marginal granular base materials. However, it was found that the asphalt emulsion with cement stabilization showed the highest performance improvement. It also was found that the addition of cement to emulsified and foamed asphalt stabilization systems significantly improved the mechanistic-climatic durability of the marginal granular base aggregate. This study demonstrated the rapid triaxial tester to be a pragmatic and cost-efficient methodology to characterize the mechanistic constitutive relations of granular base materials for performing mechanistic road structural modeling.
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 | 0.000 |
| 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 | 0.000 |
| 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".