A Fuzzy Approach To Relate FWD Surface Deflection To Laboratory Determined Resilient Moduli
Bibliographic record
Abstract
Backcalculation of pavement layer modulus by layered elastic analysis programs is the most common practice for pavement evaluation. These programs have limitations in computational algorithm that include assumptions such as layer properties and falling weight deflectometer (FWD) test conditions. FWD test results are aected by stress nonlinearity, layer thicknesses, temperature and other factors. So, layer moduli backcal- culated by these programs are not always in agreement with laboratory measured resilient modulus. A fuzzy model has been developed using the modified learning from example (MLFE) algorithm. It backcalculates the resilient modulus of pavement layers from surface deflections and layer thicknesses. To train the system, surface deflections and pavement thicknesses have been collected during FWD tests. Layer resilient moduli have been determined by laboratory tests on samples collected from field coring. This model is validated by laboratory test data. To investigate computational accuracy, it is compared to the results of the BAKFAA software and it shows the better accuracy of the fuzzy model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".