Strength and rutting characteristics of asphalt pavements in Manitoba
Bibliographic record
Abstract
This research investigates the strength and rutting performance of in-service asphalt pavements in Manitoba using a simple, modified version of the static indirect tensile (IDT) strength test. The aim of the research is to evaluate the strength and deformation properties of asphalt mixtures and relate these fundamental properties to observed rutting behaviour in the field. This represents a significant shift from traditional mix evaluation methods, which rely primarily on mix volumetric properties and empirically based tests, such as Marshall stability and flow, to assess rutting resistance. Experience gained in this area will serve to help in the selection of suitable mix designs that are more resistant to rutting. Cored samples were collected from ten representative highway sections across the province with varying age, traffic, and rut depth characteristics. Twenty-one cores were obtained from each pavement section: three from both the inner and outer wheel paths and 15 from the area between wheel paths. The cores were obtained from three randomly selected areas within each pavement section. Mix volumetrics and binder properties were determined from 12 of the core samples while three samples per site were tested for strength and performance parameters. Performance of the samples was determined using a modified form of the static indirect tensile strength test at 25oC. The specimens were loaded diametrally at a loading rate of 0.1 mm/minute until failure occurred. Miniature LVDTs mounted directly on the central portion of the sample measured the lateral and axial deformations while a load cell captured the strength data continuously throughout testing. Simple regression analysis was employed to relate the rutting data from each pavement...
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".