Assessing the Benefits of Ground Penetrating Radar Technology - Does It Improve the Accuracy of FWD Results and Overlay Design?
Bibliographic record
Abstract
Falling Weight Deflectometer (FWD) testing is an integral component of many city's pavement and asset management programs. 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 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. For the covering abstract of this conference see ITRD number 201211RT334E.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".