Advanced analysis of pavement longitudinal profiles for rehabilitation diagnostic
Bibliographic record
Abstract
In cold environments such as the one prevailing in the province of Québec (Canada), the two prominent problems associated with frost action are differential frost heave resulting from variable frost action in frost susceptible subgrade soils and frost heave occurring at shallow depths in pavements around cracks. Distortions of the surface profile resulting from frost heave can be divided into short or long wavelength distortions depending on whether or not heaving is associated with crack heaving, or differential frost heaving in the subgrade soil. Identifying the specific cause of profile deterioration during winter through routine analysis of profile and IRI information is currently very difficult to do with existing analysis tools. In order to develop new profile related diagnostic tools, a research project was undertaken on three pavement sections for which winter roughness deterioration causes are well documented. The surface profiles of the experimental sections were measured in summer and winter using an inertial profilometer. For each test section, the IRI difference (ΔIRI uf ) between winter and summer profiles, calculated every 100 m, was determined. The measured profiles were also filtered for short wavelengths (1 to 3 m) and long wavelengths (8 to 12 m) in order to determine the IRI difference (ΔIRI f ) between winter and summer filtered profiles. The value of the ratio ΔIRI f /ΔIRI uf calculated for each passing wavelength band was used as the main indicator within a decision chart. The proposed method can adequately differentiate the sections affected by deep frost heaving from those affected by shallow crack heaving.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 0.001 |
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".