Sensitivity Analysis of Field-to-Laboratory Subgrade Conversion Factors with AASHTOWare Pavement ME Design
Bibliographic record
Abstract
The new AASHTOWare Pavement ME design software incorporates conversion factors into its procedure to correlate the field subgrade soil resilient modulus, measured with the falling-weight deflectometer, with the laboratory equivalent modulus for use in performance prediction. The national conversion factors recommended for use with Pavement ME were determined on the basis of data obtained from the Long-Term Pavement Performance database and were not necessarily accurate for all regions and soil types. Sensitivity analysis results are presented for the effect of field-to-laboratory subgrade modulus conversion factors on the performance of three types of pavement structures in Ontario, Canada: conventional flexible, deep-strength flexible, and composite. This work included simulation of real-world case studies extracted from the Ontario provincial pavement management system (Ontario PMS2) database. Three projects were selected for this study–-Highway 6, Highway 401, and Queen Elizabeth Way–-all located in southwestern Ontario. Required inputs including traffic, material characteristics, and layer thickness information were extracted from PMS2 and used to set up the projects in AASHTOWare Pavement ME. The AASHTOWare Pavement ME was executed with ranges of field-to-laboratory conversion factors for different pavement types. Statistical analyses were carried out to evaluate the effect of changing conversion factors on predicted performance. Study results suggest that the predicted performance of conventional flexible pavement is significantly affected by the subgrade modulus conversion factor. However, the impact of subgrade modulus conversion factors on predicted performance is insignificant for composite and deep-strength flexible pavement sections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".