Mechanistic road upgrade structural design evaluation using rapid triaxial frequency sweep testing and linear elastic modeling
Bibliographic record
Abstract
Saskatchewan Ministry of Highways and Infrastructure employ modified Shell nomographs for flexible pavement thickness design. These modified design nomographs for typical 1970s Saskatchewan field state conditions are based on the local calibration of Shell design curves. However, the modified Shell nomographs are not applicable for the design of recycled pavement systems, particularly full depth reclamation and granular base strengthened systems. This paper presents a mechanistic based thickness design methodology using linear elastic road modeling and a field validation study based on granular base stabilization and strengthening pilot project on Control Section (C.S.) 15-11 in Saskatchewan. The mechanistic methodology of this research included triaxial frequency sweep dynamic modulus and Poisson’s ratio characterization of various strengthened materials taken from the C.S. 15-11 test sections, pavement structural modeling using linear elastic theory, and calculating critical pavement design strain responses. As demonstrated in this research, strengthened pavement structures can be evaluated and designed based on mechanistic testing and linear elastic modeling results. The modeling results revealed rutting and shoving failure concurred with the field performance observed. Predicted primary deflection responses also concurred with those quantified in the field using a non-destructive falling weight deflectometer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".