Method for Analyzing Time-Series GPR Data of Concrete Bridge Decks
Bibliographic record
Abstract
Ground-penetrating radar (GPR) has been extensively studied in North America as a nondestructive evaluation (NDE) technology for inspection of concrete bridge decks. With current practices, however, GPR has only proven to be an indicator of potential damage. Basically, to obtain the condition map for a concrete bridge deck, one would try to analyze one-time GPR data based mostly on the relative difference between reflection amplitudes at the top rebar layer. With a hypothesis that time-series GPR data can provide better information on bridge deck deterioration progression, this study investigates and proposes a new method to interpret those time-series data sets. Based on a correlation coefficient between A-scans, the proposed methodology was implemented and validated for a bare concrete bridge deck in New Jersey. The map provided by the proposed method clearly shows deterioration progression between the two consecutive scans, whereas the traditional analysis technique using the top rebar amplitude suggests unreasonable improvement of the deck condition over time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".