Determining Ground Penetrating Radar Amplitude Thresholds for the Corrosion State of Reinforced Concrete Bridge Decks
Bibliographic record
Abstract
ABSTRACT The most expensive part to maintain throughout the lifespan of a reinforced concrete bridge is the deck, largely because rehabilitation occurs after degradation is visible on a surface. However, the mechanisms that are the cause of deterioration, such as reinforcing bar corrosion, are initiated long before damage is detected via visual inspection. If rebar corrosion can be detected in its early stages, before severe deterioration has resulted, maintenance costs can be significantly reduced and the life cycle extended. Recent studies have shown that ground penetrating radar (GPR) rebar reflection amplitude attenuation correlates with active corrosion in reinforced concrete bridge decks. A significant advantage of GPR over other non-destructive evaluation (NDE) methods is its ability to be operated at highway speeds so that traffic is not disrupted. However, a well-defined GPR amplitude threshold allowing the operator to distinguish non-corroded from corroded areas of the deck has yet to be established. Because reinforcing steel corrosion is the most predominant cause of bridge deck deterioration, this research seeks to quantify the thresholds relating GPR signal amplitudes and rebar corrosion. One bridge deck removed from service, seventeen artificially corroded slabs, and one in-service bridge deck were analyzed using GPR and half-cell potential (HCP), which measures the amount of active corrosion and is currently considered the standard NDE method. A significant correlation between these two methods was found for each case. To systematically determine a threshold for the GPR so that deteriorated areas of the deck can be identified, receiver operating characteristic (ROC) curves were utilized. With an accuracy of over 87% for each scenario, this method clearly demonstrates the use of GPR for distinguishing corrosion in bridge decks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".