Phenomena and Conditions in Bridge Decks That Confound Ground-Penetrating Radar Data Analysis
Bibliographic record
Abstract
Ground-penetrating radar (GPR) has gained a reputation for providing a fast and generally effective means of assessing and prioritizing reinforced concrete bridge deck delamination repairs on a network level. A collaborative research project between Dalhousie University and the Nova Scotia Department of Transportation and Public Works studied the use of GPR for asphalt overlaid reinforced concrete deck condition assessment. In some instances, differences observed between the GPR-based predictions and the actual quantities of necessary repairs resulted in significant costs for deck repair projects studied during this research project. These differences were caused by phenomena and conditions typical of structures in service and may confound the GPR data analysis. These phenomena and conditions include properties and condition of the asphalt concrete overlay, environmental effects pertaining to moisture, deck structure, extent of deterioration, and the method of detecting and delineating repair areas on the exposed deck surface. When these conditions and effects are expected in survey results, GPR can provide a powerful tool for network-level management of deck repairs. This paper provides a discussion of these phenomena and their effects on the resulting GPR surveys.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".