Effect of the input of structural parameters’ uncertainties and analysts’ arbitrary decisions on the results of backcalculated pavement materials’ resilient moduli
Bibliographic record
Abstract
This study examines how uncertainties in the input of structural parameters and analysts’ decisions affect the estimated resilient moduli of pavement layers obtained through backcalculation. Three structures with varying asphalt layer thicknesses were created to predict deflectometric basins, using the falling weight deflectometer (FWD). Backcalculations were then performed with variations in layer thicknesses (within the 5% range), Poisson’s ratio of each layer, and seed moduli of each layer. This was repeated 60 times for each structure with randomized parameter variations. Resilient moduli from backcalculations were compared to reference structures, revealing relative errors. Statistical analysis showed uncertainties in layer thicknesses and that Poisson’s ratios impact backcalculation results by 3%–33%, depending on asphalt layer thickness. This variability could alter conclusions in pavement assessment. This is a case study with real deflectometric basins validated theoretical findings using field data.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".