Dynamic interpretation of falling weight deflectometer tests on flexible pavements using the spectral element method: backcalculation
Bibliographic record
Abstract
A robust backcalculation methodology that uses the Levenberg–Marquardt iterative minimization technique is presented to identify the value of unknown layer parameters from falling weight deflectometer (FWD) tests using a dynamic approach based on the spectral element method. Backcalculation is performed in the time-domain with 20 observations on each deflection history. The efficiency of the proposed methodology is demonstrated by interpreting FWD tests on three flexible pavements that cover a variety of structures, soil, and bedrock conditions. Results indicate that the dynamic approach is capable of simulating quite well the measured deflection histories using effective backcalculated moduli. In addition, comparison of critical strains between static and dynamic interpretation of FWD tests indicates that both approaches predict similar traction strains at the bottom of the asphalt concrete layer. However, the prediction of the compression strain in the subgrade with the static approach is erratic compared with the dynamic method.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".