Assessing the benefits of Ground Penetrating Radar technology - Does it improve the accuracy of FWD results and Overlay design?
Why this work is in the frame
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Bibliographic record
Abstract
Falling Weight Deflectometer (FWD) testing is an integral component of many city's pavement and asset management programs. FWD testing is used by cities for both network and project level testing to assess the in-situ strength of the pavement structure and underlying subgrade soils. For project level testing, the correct Maintenance, Rehabilitation, or Reconstruction (M, R &R) strategy can be determined using deflection results obtained from the FWD. One of the key data requirements for analyzing FWD data through " Backcalculation" is accurate pavement layer data. Many cities rely solely on as built data or core/bore data as an input for FWD Backcalculation. Since pavement thickness, material types, and composition can vary along the length of a roadway, some level of uncertainty is introduced in the analysis and design due to the lack of a continuous profile. More recently, Ground Penetrating Radar (GPR) technology is being used to provide a continuous layer profile and enhance FWD results. As a part of this study, over 150 ln-km of roadways in Calgary, Alberta were surveyed with the GPR and FWD in 2010 and 2011. A number of cores were also advanced on all the surveyed roads for calibration purposes. The FWD data was backcalculated using the AASHTO 1993 methodology using three sets of pavement layer data. The first set was based solely on as built data; the second set relied on core data alone; and the third source relied on GPR data calibrated with cores. The required Overlay Thickness was calculated based on the three sets of pavement layer data and compared. The results of the study demonstrate the benefits of using accurate pavement layer data for FWD analysis and helps reduce the chance of under or over designing the pavement M, R & R strategy. The study also demonstrates the value of collecting GPR data for municipal project level pavement evaluation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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