Estimating Base Layers and Subgrade Modulii for ME Pavement Design in Manitoba
Bibliographic record
Abstract
The assessment of the structural adequacy of an existing pavement scheduled for rehabilitation is an important aspect of pavement rehabilitation design. Non-destructive testing (NDT) has been widely used to determine the design inputs for existing pavement layers in rehabilitation design. The Falling Weight Deflectometer (FWD) is used by different agencies for the non-destructive evaluation of existing pavement layers and subgrade properties. In addition, the deflection basin from the FWD can be applied to select an appropriate rehabilitation strategy. The backcalculated elastic properties of each layer in the pavement and the subgrade from the FWD testing are required for Level 1 and 2 inputs in rehabilitation design when using the AASHTOWare Pavement ME Design program. This paper focuses on the backcalculation of pavement layers and subgrade modulii using various methods. The backcalculated resilient modulii (MR) of the base and subgrade from these procedures are compared with modulii from the forward calculation method. This forward calculation method was developed under the Federal Highway Administration’s project for reviewing Long-Term Pavement Performance (LTPP) backcalculation data. The calculated modulii are also compared to the laboratory determined resilient modulus for similar materials. The highway sections used in the study are the same test sections proposed to be used to calibrate the Pavement ME distress models in Manitoba. The selected modulii of the pavement layers and subgrade will be used as inputs in the calibration process.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".