Modeling the In Situ Performance of Granular Materials Stabilized with Cement
Bibliographic record
Abstract
Since City of Saskatoon (COS) current design methods do not directly incorporate stabilization materials, this study used a three dimensional non-linear orthotropic computational road structure model to measure the performance of pavement structure stabilized base course layers. The objective of this project was to investigate the effects of cement stabilization for granular material and subgrade soil, within a typical COS road structure. The cross section used in this study was a typical COS local road structure, composed of 45 mm hot mix asphalt concrete (HMAC) on 225 mm granular base, built directly on top of in situ subgrade. The cross section was analysed with two percent cement stabilization added to the granular base layer and three percent cement stabilization added to the top 300 mm of the in situ subgrade. Gyratory compaction and triaxial frequency sweep analysis of the materials were conducted at realistic field state conditions to determine the mechanistic material constitutive properties used in the structural road model. The cement stabilized granular base layer examined in the study showed improved shear strain and horizontal strain behaviour when compared to the unstabilized granular base layer. This improvement confirms that cement stabilization of granular base materials has an enhanced primary response. This study demonstrated that COS pavement designs are highly dependent on subgrade type and condition state and that cement stabilization of subgrade materials can improve the structural primary response of the subgrade layer. For the covering abstract of this conference see ITRD record number 201211RT334E.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".