Bibliographic record
Abstract
To highlight the impact of subgrade soil variability on the pseudo-profile and roughness at the end of road construction, a research program has been carried out at the Laboratoire sur les chaussées et matériaux bitumineux at École de technologie supérieure (ÉTS). The analysis is intended to highlight the variability of materials’ characteristics according to the longitudinal and vertical axes of the road. The proposed approach aims to calculate potential settlement variations through a global numerical simulation of a road section, in a bid to identify the expected pseudo-profile and to quantify the roughness quality by calculating the international roughness index (IRI). The proposed methodology was tested at a new road construction project in Québec. Here, the results of the simulations are compared to the details of the actual pseudo-profile obtained at the end of road construction for the project under study. It is shown, through the finite difference modeling of the mechanical behavior of the subgrade soil (FLAC calculation code), that the long wavelength pseudo-profile of the road at the end of construction can be determined, and that it is strongly influenced by subgrade soil variability. This article presents a second analysis carried out by Vaillancourt and Perraton in 2015 as part of a study of the impact of subgrade soils on pavement roughness.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".