Static and Dynamic Backcalculation Analyses of an Inverted Pavement Structure
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Bibliographic record
Abstract
A crucial part of any maintenance strategy is an intricate understanding of the material characteristics of the pavement, so that the current level of damage may be accurately assessed and an appropriate plan implemented. Advances in the precision to which these parameters can be determined, as well as improvements in how these results are interpreted under varying conditions of measurement and analysis, are essential in the effective execution of a maintenance strategy. Results from Falling Weight Deflectometer (FWD), which is a Non-Destructive Testing (NDT) device, can be used to predict elastic modulus of any layer by comparing measured deflection data to calculated values through an iterative process referred to as back-calculation. This paper presents a comparison between static and dynamic back-calculation procedures, specifically with regard to typical South African inverted pavements. The analysis indicates a dynamic analysis provides results of greater accuracy than a static analysis, although the effect of the difference requires further investigation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it