Effectiveness of Ground Penetrating Radar in Predicting Deck Repair Quantities
Bibliographic record
Abstract
Ground penetrating radar (GPR) was examined as an alternative or supplement to visual inspection methods for predicting reinforced concrete bridge deck repairs. Visual inspection has frequently resulted in grossly inaccurate estimates of repairs causing large maintenance cost overruns. GPR-predicted deteriorations were compared to deterioration detected using the chain drag and half-cell potential methods on 24 asphalt covered reinforced concrete decks exhibiting a broad spectrum of deterioration levels. The differences among the deterioration quantities resulting from these surveys were normalized for comparison with respect to the deterioration area and deck size. Large proportions of all decks surveyed containing less than 10% and more than 50% deterioration of the total deck surface area (as measured by chain drag) exhibited significant differences between the GPR and both ground-truth survey quantities. Insignificant differences between GPR predictions and the ground-truth results were observed for six out of seven decks exhibiting deterioration levels between 10 and 50% (by chain drag). It is concluded from this investigation that a combination of visual inspection and GPR inspection surveys for all decks can improve repair estimates and reduce the occurrence of gross underestimates of repair quantities.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".